Method and apparatus for determining state of health of battery cell, and device, storage medium and program product

Abstract

The present application relates to a method and apparatus for determining the state of health (SOH) of a battery cell, and a device, a storage medium and a program product. The method comprises: acquiring a current value sequence and a voltage value sequence of a battery cell during variable-current charging; on the basis of the current value sequence and the voltage value sequence of the battery cell, determining a characteristic voltage sequence of the battery cell under a constant simulated current; on the basis of the characteristic voltage sequence, determining a parameter correspondence of the battery cell, and on the basis of the parameter correspondence, determining phase transition characteristic data corresponding to each phase transition point of the battery cell; and on the basis of the phase transition characteristic data corresponding to each phase transition point of the battery cell, determining the SOH of the battery cell. Therefore, a solution capable of estimating the SOH when the current varies during charging is provided.

Description

Methods, apparatus, equipment, storage media, and procedures for determining the health status of battery cells. Cross-referencing

[0001] This application incorporates Chinese Patent Application No. 2024120000578, filed on December 31, 2024, entitled “Method, Apparatus, Device, Storage Medium and Program Product for Determining Cell Health Status”, which is incorporated herein by reference in its entirety. Technical Field

[0002] This application relates to the field of battery technology, and in particular to a method, apparatus, device, storage medium, and program product for determining the health status of a battery cell. Background Technology

[0003] Typically, batteries experience performance degradation during use. To ensure the safe operation of electrical devices or energy storage systems, it is necessary to estimate the State of Health (SOH) of the battery cells. Estimating the SOH of cells during charging is currently a key research focus.

[0004] In related technologies, the SOH of a battery cell is estimated using either incremental capacity analysis (ICA) or differential voltage analysis (DVA) algorithms based on the current and voltage curves of the cell during charging.

[0005] However, the ICA or DVA methods are not applicable to SOH estimation under current variations during charging. Therefore, how to estimate SOH under current variations during charging has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] In view of the above problems, this application provides a method, apparatus, device, storage medium and program product for determining the state of health of a battery cell, which can estimate the state of health (SOH) under the condition of current variation during charging.

[0007] Firstly, this application provides a method for determining the health status of a battery cell, the method comprising:

[0008] Obtain the current and voltage value sequences of the battery cell during variable current charging;

[0009] Based on the current and voltage value sequences of the battery cell, the characteristic voltage sequence of the battery cell under constant simulated current is determined; the constant simulated current is the preset charging current.

[0010] The parameter correspondence of the battery cell is determined based on the characteristic voltage sequence, and the phase transition characteristic data corresponding to each phase transition point of the battery cell is determined based on the parameter correspondence.

[0011] The health status of the battery cell is determined based on the phase transition characteristic data corresponding to each phase transition point of the cell.

[0012] The method provided in this embodiment obtains the current value sequence and voltage value sequence of the battery cell during variable current charging, determines the characteristic voltage sequence of the battery cell under constant simulated current based on the current value sequence and voltage value sequence, determines the parameter correspondence of the battery cell based on the characteristic voltage sequence, determines the phase transition characteristic data corresponding to each phase transition point of the battery cell based on the parameter correspondence, and determines the health status of the battery cell based on the phase transition characteristic data corresponding to each phase transition point of the battery cell. Thus, it provides a scheme that can estimate the state of health (SOH) of the battery cell under current variation during charging.

[0013] In one embodiment, the characteristic voltage sequence of the battery cell under a constant analog current is determined based on the current value sequence and voltage value sequence of the battery cell, including:

[0014] Multiple charging periods are determined based on the current value sequence;

[0015] Obtain the current value subsequence and voltage value subsequence corresponding to each charging period;

[0016] Based on the current and voltage value subsequences of at least two charging periods, determine the characteristic voltage subsequence of the cell under constant simulated current;

[0017] The characteristic voltage sequence is determined based on all characteristic voltage subsequences of the battery cell under constant simulated current.

[0018] The method provided in this embodiment determines multiple charging periods based on current value sequences, obtains current value subsequences and voltage value subsequences corresponding to each charging period, determines the characteristic voltage subsequence of the battery cell under a constant simulated current based on the current value subsequences and voltage value subsequences of at least two charging periods, and determines the characteristic voltage sequence based on all characteristic voltage subsequences of the battery cell under a constant simulated current. The method gradually transitions the voltage value subsequences with large transitions in at least two charging periods to relatively smooth characteristic voltage subsequences, ultimately obtaining a smooth characteristic voltage sequence. Therefore, the phase transition characteristic data determined based on the characteristic voltage sequence is more accurate.

[0019] In one embodiment, the characteristic voltage sequence of the battery cell under a constant analog current is determined based on the current value sequence and voltage value sequence of the battery cell, including:

[0020] Based on the current value sequence, voltage value sequence, preset charging current, and pre-trained model, a characteristic voltage sequence is obtained.

[0021] The method provided in this embodiment obtains a characteristic voltage sequence based on the current value sequence, voltage value sequence, preset charging current, and pre-trained model. It transitions the voltage value sequence with large transitions to a relatively smooth characteristic voltage sequence, thereby making the health status determined based on the characteristic voltage sequence more accurate.

[0022] In one embodiment, a characteristic voltage sequence is obtained based on the current value sequence, the voltage value sequence, a preset charging current, and a pre-trained model, including:

[0023] The current value sequence, voltage value sequence, and preset charging current are input into the pre-trained model to obtain multiple feature voltage sub-sequences under the preset charging current;

[0024] The characteristic voltage sequence is obtained based on each characteristic voltage subsequence.

[0025] The method provided in this embodiment obtains multiple characteristic voltage subsequences under the preset charging current by inputting a current value sequence, a voltage value sequence, and a preset charging current into a pre-trained model, and then obtains a characteristic voltage sequence based on each characteristic voltage subsequence. Since multiple characteristic voltage subsequences can be directly obtained through the pre-trained model, the efficiency of obtaining multiple characteristic voltage subsequences is improved, thereby improving the efficiency of obtaining the characteristic voltage sequence.

[0026] In one embodiment, the characteristic voltage subsequence of the battery cell under a constant analog current is determined based on the current value subsequence and voltage value subsequence of at least two charging periods, including:

[0027] Based on the current and voltage subsequences of any multiple adjacent charging periods, the characteristic voltage subsequence of the battery cell under constant simulated current is determined.

[0028] The method provided in this embodiment can determine the characteristic voltage subsequence of the battery cell under constant simulated current based on the current value subsequence and voltage value subsequence of any adjacent multiple charging periods. It can capture the current value subsequence and voltage value subsequence before or after multiple consecutive current change moments, and is more adaptable to the internal mechanism changes of the battery cell during the corresponding charging period, thereby obtaining a relatively smooth transition characteristic voltage subsequence.

[0029] In one embodiment, the characteristic voltage subsequence of the battery cell under a constant analog current is determined based on the current value subsequence and voltage value subsequence of at least two charging periods, including:

[0030] Based on the current and voltage subsequences of any two adjacent charging periods in multiple charging periods, the characteristic voltage subsequence of the battery cell under constant simulated current is determined.

[0031] The method provided in this embodiment determines the characteristic voltage subsequence of the battery cell under constant simulated current by using the current value subsequence and voltage value subsequence of any two adjacent charging periods in multiple charging periods. Since one characteristic voltage subsequence corresponds to only two adjacent charging periods, any two adjacent charging periods can accurately capture the current value subsequence and voltage value subsequence before or after the current change moment. Moreover, the current change time of two adjacent charging periods is short, and the internal mechanism change of the battery cell is relatively small. Thus, based on the current value subsequence and voltage value subsequence of two adjacent charging periods, several accurate and smoothly transitioning characteristic voltage subsequences can be obtained.

[0032] In one embodiment, multiple charging periods are determined based on a sequence of current values, including:

[0033] When the battery cell is in a multi-stage stepped current charging state, obtain the current change moments in the current value sequence.

[0034] A preset period of time before or after each current fluctuation is considered as a charging period.

[0035] The method provided in this embodiment obtains each current change moment in the current value sequence when the battery cell is in a multi-stage stepped current charging state, and takes the preset time period before or after each current change moment as a charging time period, so that the obtained charging time period is more in line with the actual stepped current charging scenario. Moreover, the determination of the current change moment is relatively simple and quick. Therefore, the efficiency of obtaining the charging time period can be improved by determining the charging time period through the current change moment.

[0036] In one embodiment, multiple charging periods are determined based on a sequence of current values, including:

[0037] When the battery cell is in a working state of charging with arbitrary variable current, obtain the current value at each time point in the current value sequence;

[0038] If the difference between the current value at a given time point and the current value at the previous time point is less than or equal to a first preset threshold, then that time point is considered a valid time point.

[0039] A series of consecutive effective time points are considered as a charging period.

[0040] The method provided in this embodiment acquires the current values ​​at various time points in a current value sequence when the battery cell is in an arbitrary variable current charging state. If the difference between the current value at a time point and the current value at the previous time point is less than or equal to a first preset threshold, then that time point is considered a valid time point. Consecutive valid time points are considered as a charging period, making the obtained charging period more consistent with the actual arbitrary variable current charging scenario. Furthermore, this method of using a time point as a valid time point when the difference between the current value at a time point and the current value at the previous time point is less than or equal to the first preset threshold is relatively simple and fast, thus increasing the probability of obtaining valid time points and consequently increasing the probability of obtaining a charging period.

[0041] In one embodiment, the characteristic voltage subsequence of the battery cell under constant simulated current is determined based on the current value subsequence and voltage value subsequence of any two adjacent charging periods in a plurality of charging periods, including:

[0042] Based on the current value subsequence of at least two charging periods among multiple charging periods, determine the total charging capacity corresponding to at least two charging periods;

[0043] Based on the total charging capacity corresponding to at least two charging periods, determine the characteristic current subsequence of the battery cell under constant simulated current;

[0044] Based on the current and voltage subsequences of at least two charging periods, and the characteristic current subsequence of the cell under constant analog current, determine the characteristic voltage subsequence of the cell under constant analog current.

[0045] The method provided in this embodiment determines the total charging capacity corresponding to at least two charging periods based on the current value subsequences of at least two charging periods. Based on the total charging capacity corresponding to the at least two charging periods, it determines the characteristic current subsequence of the battery cell under a constant simulated current. Furthermore, based on the current and voltage value subsequences of the at least two charging periods, and the characteristic current subsequence of the battery cell under a constant simulated current, it determines the characteristic voltage subsequence of the battery cell under a constant simulated current. This achieves the goal of converting the current and voltage value subsequences under varying current conditions into a characteristic voltage subsequence under simulated current conditions without changing the total charging capacity, thus eliminating the problem of voltage jumps in the voltage value curve as a function of charging capacity under varying current conditions.

[0046] In one embodiment, the characteristic voltage subsequence of the battery cell under constant analog current is determined based on the current value subsequence and voltage value subsequence of at least two charging periods, and the characteristic current subsequence of the battery cell under constant analog current, including:

[0047] Input the current and voltage subsequences of at least two charging periods into the battery model to calibrate the parameters of the battery model and obtain the target battery model after parameter calibration.

[0048] The characteristic current subsequence of the cell under constant simulated current is input into the target battery model to obtain the characteristic voltage subsequence of the cell under constant simulated current.

[0049] The method provided in this embodiment calibrates the parameters of a battery model by inputting current and voltage subsequences from at least two charging periods into the model, resulting in a calibrated target battery model. The characteristic current subsequence of the battery cell under constant simulated current is then input into the target battery model to obtain a characteristic voltage subsequence of the battery cell under constant simulated current. The battery model is calibrated online based on the current and voltage subsequences from at least two charging periods. The characteristic voltage subsequence is then determined using the online-calibrated target battery model and the characteristic current subsequence obtained from the current subsequences of at least two charging periods, making the characteristic voltage subsequence more consistent with the actual charging scenario of the battery cell and resulting in a smoother transition.

[0050] In one embodiment, the parameter correspondence includes a first characteristic curve, and the phase transition characteristic data corresponding to each phase transition point of the battery cell are determined according to the parameter correspondence, including:

[0051] Obtain the preset feature curve segment;

[0052] The preset characteristic curve segments are matched with the first characteristic curve to determine the phase transition characteristic data corresponding to each phase transition point.

[0053] The method provided in this embodiment obtains a preset feature curve segment and matches the preset feature curve segment with a first feature curve to determine the phase transition feature data corresponding to each phase transition point. Since the preset feature curve segment can accurately reflect the phase transition point, the phase transition feature data corresponding to the phase transition point obtained based on the preset feature curve segment is more accurate.

[0054] In one embodiment, a preset feature curve segment is matched with a first feature curve to determine the phase transition feature data corresponding to each phase transition point, including:

[0055] Determine the corresponding rectangular window based on each preset feature curve segment;

[0056] Based on each rectangular window and the first preset number of segments, the first feature curve is divided into multiple first candidate curve segments along the horizontal axis.

[0057] From multiple first candidate curve segments, identify the target candidate curve segment that matches the preset feature curve segment;

[0058] The phase transition characteristic data corresponding to each phase transition point are determined based on each candidate curve segment.

[0059] The method provided in this embodiment determines corresponding rectangular windows based on each preset feature curve segment. Based on each rectangular window and a first preset number of segments, the first feature curve is divided into multiple first candidate curve segments along the horizontal axis. From these multiple first candidate curve segments, target candidate curve segments that match the preset feature curve segments are determined. Phase transition feature data corresponding to each phase transition point is determined based on each target candidate curve segment. By dividing the first feature curve into finer-grained first candidate curve segments, and by gradually matching each fine-grained first candidate curve segment with the feature curve segment, the target candidate curve segments can be determined more accurately and quickly, thereby determining the phase transition feature data more accurately and quickly.

[0060] In one embodiment, determining a target candidate curve segment that matches a preset feature curve segment from a plurality of first candidate curve segments includes:

[0061] For each first candidate curve segment, the degree of matching between the curve features of the first candidate curve segment and the curve features of the preset characteristic curve segment is determined based on the characteristic voltage value of the first data point on the first candidate curve segment and the phase transition voltage value of the second data point on the preset characteristic curve segment; the first data point corresponds to the second data point.

[0062] Based on the matching degree of each first candidate curve segment, a target candidate curve segment that matches the preset feature curve segment is determined from multiple first candidate curve segments.

[0063] The method provided in this embodiment determines the degree of matching between the curve features of the first candidate curve segment and the curve features of the preset characteristic curve segment based on the characteristic voltage value of the first data point on the first candidate curve segment and the phase transition voltage value of the second data point on the preset characteristic curve segment. Based on the matching degree corresponding to each first candidate curve segment, a target candidate curve segment that matches the preset characteristic curve segment is determined from multiple first candidate curve segments. This makes the phase transition point determined based on the target curve segment more accurate, improves the accuracy of the phase transition feature data corresponding to the determined phase transition point, and thus improves the accuracy of the maximum capacity determined based on the phase transition feature data and the accuracy of the health status estimation.

[0064] In one embodiment, the degree of matching between the curve features of the first candidate curve segment and the curve features of the preset characteristic curve segment is determined based on the characteristic voltage value of a first data point on the first candidate curve segment and the phase transition voltage value of a second data point on a preset characteristic curve segment, including:

[0065] The characteristic voltage value of the first data point in the first candidate curve segment is transformed to obtain the target voltage value.

[0066] Calculate the voltage difference between the target voltage value of the first data point and the phase transition voltage value of the corresponding second data point;

[0067] The degree of matching between the curve features of the first candidate curve segment and the curve features of the preset feature curve segment is determined based on the voltage difference.

[0068] The method provided in this embodiment converts the characteristic voltage value of the first data point in the first candidate curve segment to obtain the target voltage value, calculates the voltage difference between the target voltage value of the first data point and the phase transition voltage value of the corresponding second data point, and determines the degree of matching between the curve features of the first candidate curve segment and the curve features of the characteristic curve segment based on the voltage difference. This facilitates the determination of the target candidate curve segment based on the degree of matching corresponding to each first candidate curve segment, thereby improving the accuracy of determining the phase transition point and the accuracy of the phase transition feature data corresponding to the phase transition point.

[0069] In one embodiment, based on the matching degree corresponding to each first candidate curve segment, a target candidate curve segment that matches a preset feature curve segment is determined from a plurality of first candidate curve segments, including:

[0070] The first candidate curve segment corresponding to the highest matching degree is taken as the target candidate curve segment that matches the preset feature curve segment.

[0071] The method provided in this embodiment improves the accuracy of the obtained target candidate curve segments by using the first candidate curve segment corresponding to the maximum matching degree as the target candidate curve segment that matches the feature curve segment, thereby improving the accuracy of the phase transition feature data corresponding to the obtained phase transition point.

[0072] In one embodiment, the parameter correspondence includes a first characteristic curve, and the phase transition characteristic data corresponding to each phase transition point of the battery cell are determined according to the parameter correspondence, including:

[0073] Obtain the second characteristic curve of the reference cell;

[0074] Based on the length of the second feature curve in the horizontal direction and the second preset number of parts, the first feature curve is divided into multiple second candidate curve segments along the horizontal direction.

[0075] The second characteristic curve is matched with multiple second candidate curve segments to determine the phase transition characteristic data corresponding to each phase transition point.

[0076] The method provided in this embodiment obtains a second characteristic curve of a reference cell. Based on the length of the second characteristic curve along the horizontal axis and a second preset number of segments, the first characteristic curve is divided into multiple second candidate curve segments along the horizontal axis. The second characteristic curve is then matched with these multiple second candidate curve segments to determine the phase transition characteristic data corresponding to each phase transition point. By dividing the first characteristic curve into finer-grained second candidate curve segments, and by gradually matching each fine-grained second candidate curve segment with the second characteristic curve, the phase transition characteristic data can be determined more accurately and quickly.

[0077] In one embodiment, the second characteristic curve is matched with multiple second candidate curve segments to determine the phase transition characteristic data corresponding to each phase transition point, including:

[0078] For each second candidate curve segment, the average value of the ordinates of all points on the second characteristic curve is made to be equal to the average value of the ordinates of all points on the second candidate curve segment. The difference between the second characteristic curve with the equal average value and the second candidate curve segment is calculated to obtain the error value.

[0079] The phase transition characteristic data corresponding to each phase transition point are determined by matching the error values ​​corresponding to each second candidate curve segment.

[0080] The method provided in this embodiment involves, for each second candidate curve segment, averaging the ordinates of all points on the second characteristic curve to the average ordinates of all points on the second candidate curve segment. The difference between the averaged second characteristic curve and the second candidate curve segment is then calculated to obtain an error value. Matching is performed based on the error values ​​corresponding to each second candidate curve segment to determine the phase transition feature data corresponding to each phase transition point. Matching with each second candidate curve segment at a fine-grained level improves the matching accuracy, thereby enhancing the accuracy of the obtained phase transition feature data.

[0081] In one embodiment, the phase transition characteristic data corresponding to each phase transition point is determined by matching the error values ​​corresponding to each second candidate curve segment, including:

[0082] Based on the minimum error value, the corresponding second candidate curve segment is matched to determine the phase transition characteristic data corresponding to each phase transition point.

[0083] The method provided in this embodiment determines the phase transition feature data corresponding to each phase transition point by matching the corresponding second candidate curve segment based on the minimum error value. Since the second candidate curve segment corresponding to the minimum error value is the curve segment most similar to the second feature curve, the phase transition feature data corresponding to the phase transition point determined based on the most similar curve segment is more accurate.

[0084] In one embodiment, the parameter correspondence includes a first characteristic curve, and the phase transition characteristic data corresponding to each phase transition point of the battery cell are determined according to the parameter correspondence, including:

[0085] The first characteristic curve is processed using a preset analysis algorithm to obtain phase transition peak information, and the phase transition characteristic data corresponding to each phase transition point is determined based on the phase transition peak information.

[0086] The method provided in this embodiment processes the first characteristic curve using a preset analysis algorithm to obtain phase transition peak information, and determines the phase transition characteristic data corresponding to each phase transition point based on the phase transition peak information. This can improve the accuracy of the phase transition characteristic data corresponding to multiple phase transition points, so that a more accurate SOH estimation can be performed based on the phase transition characteristic data corresponding to multiple phase transition points.

[0087] In one embodiment, the health status of the battery cell is determined based on the phase transition characteristic data corresponding to each phase transition point of the cell, including:

[0088] The maximum capacity of the battery cell is determined based on the phase transition characteristic data corresponding to each phase transition point of the battery cell.

[0089] The health status of a battery cell is determined based on its maximum capacity and its rated capacity.

[0090] The method provided in this embodiment determines the maximum capacity of the battery cell based on the phase change characteristic data corresponding to each phase change point, and determines the health status of the battery cell based on the maximum capacity and the rated capacity of the battery cell. Since the phase change characteristic data can accurately reflect the changes in the health status of the battery cell, the accuracy of the health status of the battery cell obtained based on the phase change characteristic data corresponding to each phase change point is high.

[0091] In one embodiment, determining the maximum capacity of the battery cell based on phase transition characteristic data corresponding to each phase transition point of the cell includes:

[0092] The attenuation type corresponding to each phase transition point is determined based on the phase transition characteristic data corresponding to each phase transition point. The attenuation type includes rapid attenuation type or slow attenuation type.

[0093] The reference maximum capacity is determined based on the slope obtained by linear fitting of the phase transition characteristic data corresponding to phase transition points belonging to the same decay type.

[0094] The maximum capacity of the battery cell is determined based on the reference maximum capacity corresponding to each attenuation type.

[0095] The method provided in this embodiment determines the attenuation type corresponding to each phase transition point based on the state of charge corresponding to each phase transition point. Based on the slope obtained by linearly fitting the phase transition characteristic data corresponding to phase transition points belonging to the same attenuation type, a reference maximum capacity is determined. Then, the maximum capacity of the battery cell is determined based on the reference maximum capacity corresponding to each attenuation type. Because this embodiment distinguishes the attenuation type of the phase transition point, determining the reference maximum capacity based on the attenuation type, and then determining the maximum capacity of the battery cell based on the reference maximum capacity, a more accurate maximum capacity of the battery cell can be obtained. This allows for a more accurate determination of the state of charge (SOH) of the battery cell based on the maximum capacity value.

[0096] In one embodiment, determining the attenuation type corresponding to each phase transition point based on the phase transition characteristic data corresponding to each phase transition point includes:

[0097] Based on the phase transition characteristic data corresponding to each phase transition point, the attenuation type corresponding to each phase transition point is determined according to the preset cell aging law.

[0098] The method provided in this embodiment determines the attenuation type corresponding to each phase transition point based on the phase transition characteristic data corresponding to each phase transition point and in accordance with the preset cell aging law, so that the determined attenuation type conforms to the cell aging law, thereby improving the practicality and effectiveness of the obtained attenuation type.

[0099] Secondly, this application also provides a cell health status determination device, the device comprising:

[0100] The acquisition module is configured to acquire the current value sequence and voltage value sequence of the battery cell during variable current charging.

[0101] The first determining module is configured to determine the characteristic voltage sequence of the battery cell under a constant analog current based on the current value sequence and voltage value sequence of the battery cell; the constant analog current is a preset charging current;

[0102] The second determining module is configured to determine the parameter correspondence of the battery cell based on the characteristic voltage sequence, and to determine the phase change characteristic data corresponding to each phase change point of the battery cell based on the parameter correspondence.

[0103] The third determining module is configured to determine the health status of the battery cell based on the phase change characteristic data corresponding to each phase change point of the battery cell.

[0104] Thirdly, this application also provides a battery cell health status determination device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0105] Fourthly, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in any of the first aspects above.

[0106] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the first aspects above.

[0107] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0108] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are configured only to illustrate the preferred embodiments and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0109] Figure 1 is a flowchart illustrating a method for determining the health status of a battery cell according to an embodiment of this application;

[0110] Figure 2 is a flowchart illustrating a method for determining a characteristic voltage sequence provided in an embodiment of this application;

[0111] Figure 3 is a flowchart illustrating a method for determining multiple charging periods according to an embodiment of this application;

[0112] Figure 4 is a flowchart illustrating another method for determining multiple charging periods provided in an embodiment of this application;

[0113] Figure 5 is a flowchart illustrating a method for determining a characteristic voltage subsequence according to an embodiment of this application;

[0114] Figure 6 is a flowchart illustrating another method for determining characteristic voltage sub-sequences provided in an embodiment of this application;

[0115] Figure 7 is a schematic diagram of a variable current operating condition transformation provided in an embodiment of this application;

[0116] Figure 8 is one of the flowcharts illustrating the method for determining phase transition characteristic data provided in this application embodiment;

[0117] Figure 9 is a schematic diagram of a first feature curve and three feature curve segments provided in an embodiment of this application;

[0118] Figure 10 is a second schematic flowchart of the phase transition characteristic data determination method provided in the embodiments of this application;

[0119] Figure 11 is a flowchart illustrating a method for determining a target candidate curve segment according to an embodiment of this application;

[0120] Figure 12 is a flowchart illustrating a matching degree determination method provided in an embodiment of this application;

[0121] Figure 13 is a third flowchart illustrating the method for determining phase transition characteristic data provided in this application embodiment;

[0122] Figure 14 is a flowchart of the method for determining phase transition characteristic data provided in the embodiments of this application (fourth one).

[0123] Figure 15 is a schematic diagram of a process for matching a second feature curve with multiple second candidate curve segments according to an embodiment of this application;

[0124] Figure 16 is a flowchart illustrating a method for determining health status provided in an embodiment of this application;

[0125] Figure 17 is a flowchart illustrating a method for determining maximum capacity provided in an embodiment of this application;

[0126] Figure 18 is a flowchart illustrating another method for determining the health status of a battery cell provided in this application;

[0127] Figure 19 is a schematic diagram of a battery cell health status determination device provided in an embodiment of this application;

[0128] Figure 20 is a schematic diagram of a battery cell health status determination device provided in an embodiment of this application. Detailed Implementation

[0129] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0130] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the term "comprising" and any variations thereof in the specification, claims and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0131] Typically, batteries experience performance degradation during use. To ensure the safe operation of electrical devices or energy storage systems, it is necessary to estimate the State of Health (SOH) of the battery cells. Estimating SOH during charging is a particular research focus. Since a battery cell's ability to accept charging current is affected by factors such as its charge level and temperature, variable current charging is commonly used. For example, at lower charge levels and temperatures, the battery can accept a smaller charging current; as charge level and temperature gradually increase, the charging current can be gradually increased; and then gradually decreased in the later stages of charging. This approach protects battery safety, improves charging efficiency, and extends cell lifespan.

[0132] In related technologies, the SOH of a battery cell is estimated using either incremental capacity analysis (ICA) or differential voltage analysis (DVA) algorithms based on the current and voltage curves of the cell during charging.

[0133] However, the ICA or DVA methods have a limitation: they require a relatively stable charging current with a small rate of change. Unstable charging currents severely impact feature extraction and subsequent SOH estimation accuracy. Specifically, unstable charging currents lead to unstable charging voltages, causing excessive fluctuations in the voltage derivative (dV / dt or dV / dq), which is undesirable. In other words, the ICA or DVA methods are unsuitable for SOH estimation under varying current conditions during charging. Therefore, providing a solution for SOH estimation under these conditions is a pressing technical problem in this field.

[0134] To address the aforementioned technical problems, this application proposes a method for estimating State of Charge (SOH) under varying current conditions during battery cell charging. This method involves acquiring the current and voltage value sequences of the battery cell during variable current charging, determining the characteristic voltage sequence of the battery cell under constant simulated current based on these sequences, establishing parameter correspondences based on these characteristic voltage sequences, determining the phase transition characteristic data corresponding to each phase transition point of the battery cell based on these parameter correspondences, and determining the battery cell's health status based on the phase transition characteristic data corresponding to each phase transition point. In this application, the phase transition point of the battery cell refers to the temperature point or condition under which the internal materials of the battery cell undergo a phase transition during charging and discharging. A phase transition typically refers to the crystal structure change of the internal materials of the battery cell (such as positive electrode material, negative electrode material, or electrolyte) during charging and discharging; the phase transition point is the specific temperature or condition under which this change occurs. The phase transition characteristic data may include the charging capacity and state of charge corresponding to the phase transition point.

[0135] The cell health status determination method, apparatus, device, storage medium, and program product in this application embodiment can be applied to scenarios where the health status of battery cells in electrical equipment is determined during variable current charging, such as fast charging, or other health status determination scenarios, such as arbitrary variable current charging in energy storage frequency regulation and peak shaving scenarios.

[0136] For example, the batteries involved in the embodiments of this application may include, but are not limited to, lithium-ion batteries, sodium-ion batteries, such as ternary batteries or lithium iron phosphate batteries, and the battery cells and reference battery cells in the embodiments of this application may be the battery cells of the above-mentioned batteries.

[0137] To provide a clearer description of the embodiments of this application, it is illustrated in conjunction with Figure 1. Referring to Figure 1, Figure 1 is a flowchart illustrating a method for determining the health status of a battery cell according to an embodiment of this application. This method can be applied to a battery cell health status determination device. The battery cell health status determination device involved in this embodiment may include, but is not limited to, any of the following: a controller of an electrical device, a battery management system (BMS), or a server. For example, in the scenario of determining the health status of a battery cell in an electrical device during variable current charging, the battery cell health status determination device may be a controller, BMS, or server of the electrical device. The method may include the following steps S101-S104:

[0138] S101, obtain the current value sequence and voltage value sequence of the battery cell during variable current charging.

[0139] Variable current charging can include arbitrary variable current charging, multi-stage stepped current charging, and a combination of arbitrary variable current charging and multi-stage stepped current charging. Charging scenarios include constant current charging and variable current charging. The condition for constant current charging is that the fluctuation of the charging current is very small. For example, if the absolute value of the difference between the charging current at each time point during charging and the reference charging current is less than a preset value, where the preset value is a value greater than 0. Charging that does not meet the condition of constant charging current can be considered variable current charging.

[0140] The current value sequence includes a sequence of time points and the corresponding current value, and the voltage value sequence includes a sequence of time points and the corresponding voltage value. At the same time point, there is a current value in the current value sequence and a voltage value in the voltage value sequence.

[0141] The sequences involved in this application are two-dimensional sequences. For example, the current value sequence is a sequence that includes the correspondence between a series of current values ​​and time points, and the voltage value sequence is a sequence that includes the correspondence between a series of voltage values ​​and time points. The current value sequence and the voltage value sequence are sequences throughout the entire charging process in a single variable current charging operation, and the voltage value sequence is the sequence corresponding to the current value sequence. The current value sequence and the voltage value sequence can be pre-processed sequences. Pre-processing includes, for example, data filtering, jump data processing, and asynchronous data processing.

[0142] S102, based on the current value sequence and voltage value sequence of the battery cell, determine the characteristic voltage sequence of the battery cell under constant analog current; the constant analog current is the preset charging current.

[0143] The voltage value sequence can be processed based on the current value sequence, voltage value sequence, and constant simulated current of the battery cell to obtain a processed voltage value sequence, which is the characteristic voltage sequence under constant simulated current. The characteristic voltage sequence is a sequence of characteristic voltage values ​​obtained under the premise of constant simulated current. It is a relatively smooth voltage sequence obtained by transforming the voltage value sequence with large transitions under variable current charging, under the premise of constant simulated current. This relatively smooth voltage sequence is called the characteristic voltage sequence under constant simulated current.

[0144] Based on the current and voltage value sequences of the battery cell, the characteristic voltage sequence of the battery cell under constant simulated current can be determined as follows:

[0145] Method 1: Multiple charging periods can be determined based on the current value sequence, and the current value subsequence and voltage value subsequence corresponding to each charging period can be obtained. Based on the current value subsequence and voltage value subsequence of at least two charging periods, the characteristic voltage subsequence of the cell under constant simulated current can be determined. Based on all the characteristic voltage subsequences of the cell under constant simulated current, the characteristic voltage sequence can be determined.

[0146] It should be noted that, taking multi-stage stepped current charging as an example, if the number of determined charging periods is two, then the current value sequence in S101 includes two current value subsequences for each charging period. Each current value subsequence corresponds to a voltage value subsequence. Based on these two current and voltage value subsequences, the battery model can be calibrated. These subsequences can be input into the battery model to calibrate its parameters, resulting in a calibrated target battery model. Based on this target battery model, a corresponding characteristic voltage subsequence can be obtained, which is then used as the characteristic voltage sequence. The battery model can be an equivalent circuit model, an electrochemical model, or a data-driven model, etc.

[0147] Method 2 involves inputting the current value sequence, voltage value sequence, and a preset charging current into a pre-trained model to obtain multiple characteristic voltage sub-sequences under the preset charging current. A characteristic voltage sequence is then derived from each characteristic voltage sub-sequence. The pre-trained model can include Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Fully Convolutional Neural Networks (FCN) models. Alternatively, based on the scheme provided in Method 1, characteristic voltage sub-sequence samples of the battery cell under a constant simulated current sample can be determined from the current value sequence samples and voltage value sequence samples of the battery cell. The method for obtaining the characteristic voltage sub-sequence samples is the same as in Method 1. For ease of explanation, a current value sequence sample and its corresponding voltage value sequence sample are treated as a sample pair. The characteristic voltage sub-sequence samples under the constant simulated current sample are obtained using the same method described in Method 1 under S102.

[0148] For example, if three charging periods are determined based on the current value sequence sample, and a characteristic voltage subsequence sample under a constant analog current sample is determined based on the current value subsequence sample and the voltage value subsequence sample corresponding to two adjacent charging periods in the three charging periods, then the current value sequence sample can correspond to two characteristic voltage subsequence samples. That is, one sample pair can correspond to multiple characteristic voltage subsequence samples under a constant analog current sample. The multiple characteristic voltage subsequence samples corresponding to one sample pair are spliced ​​together to obtain the characteristic voltage sequence sample.

[0149] A pre-trained model can be obtained by training an initial model based on multiple sample pairs and corresponding feature voltage subsequence sample pairs. This pre-trained model is used to output multiple feature voltage subsequences based on the current value sequence, voltage value sequence, and a preset charging current, and then to obtain a feature voltage sequence based on these multiple feature voltage subsequences. Alternatively, a pre-trained model can be obtained by training an initial model based on multiple sample pairs and corresponding feature voltage sequence sample pairs. This pre-trained model is used to directly output a feature voltage sequence based on the input current value sequence, voltage value sequence, and preset charging current.

[0150] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0151] S103, determine the parameter correspondence of the cell based on the characteristic voltage sequence, and determine the phase transition characteristic data corresponding to each phase transition point of the cell based on the parameter correspondence.

[0152] The parameter correspondence of a battery cell can include, for example, the correspondence between the cell's state of charge (SOC) and its voltage value, or the correspondence between the cell's charge capacity and its voltage value. The voltage value can be a characteristic voltage value or an open-circuit voltage value. The parameter correspondence can be represented by a characteristic curve or by a data table; this application does not limit this. For example, when representing the parameter correspondence with a characteristic curve, the horizontal axis of the characteristic curve can be the state of charge (SOC), and the vertical axis can be the voltage value. Alternatively, the horizontal axis of the characteristic curve can be the charge capacity, and the vertical axis can be the voltage value. When representing the parameter correspondence in a data table format, the SOC and voltage values ​​at the same time point can be correlated, or the charge capacity and voltage values ​​at the same time point can be correlated.

[0153] Determining the parameter correspondence of a battery cell based on its characteristic voltage sequence can be achieved in the following way:

[0154] Method 1 can determine the parameter correspondence of the battery cell based on the characteristic voltage sequence and the state of charge (SOC) sequence. This parameter correspondence includes the correspondence between the characteristic voltage value and the SOC, where the characteristic voltage value is the voltage value in the characteristic voltage sequence and the SOC is the SOC in the SOC sequence.

[0155] Method 2 involves determining the open-circuit voltage sequence based on the characteristic voltage sequence. Based on the open-circuit voltage sequence and the state-of-charge (SOC) sequence, the parameter correspondence of the battery cell is determined. This correspondence includes the relationship between the open-circuit voltage value and the SOC, where the open-circuit voltage value is the voltage value in the open-circuit voltage sequence, and the SOC is the SOC in the SOC sequence. In some embodiments, the open-circuit voltage can be calculated using adaptive algorithms such as least squares or Kalman filtering, based on the current value sequence, voltage value sequence, and corresponding time information.

[0156] Method 3 can determine the parameter correspondence of the battery cell based on the characteristic voltage sequence and the charging capacity sequence. This parameter correspondence includes the correspondence between the characteristic voltage value and the charging capacity. The characteristic voltage value is the voltage value in the characteristic voltage sequence, and the SOC is the charging capacity in the charging capacity sequence.

[0157] Method 4: The open-circuit voltage sequence can be determined based on the characteristic voltage sequence. Based on the open-circuit voltage sequence and the charging capacity sequence, the parameter correspondence of the battery cell can be determined. This parameter correspondence includes the correspondence between the open-circuit voltage value and the charging capacity. The open-circuit voltage value is the voltage value in the open-circuit voltage sequence, and the charging capacity is the charging capacity in the charging capacity sequence.

[0158] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0159] Based on the parameter correspondence of the battery cell, the phase transition characteristic data corresponding to each phase transition point of the battery cell can be determined.

[0160] In one possible implementation, a pre-defined analysis algorithm can be used to process the parameter correspondences to obtain phase transition peak information, and based on the phase transition peak information, the phase transition characteristic data corresponding to each phase transition point can be determined. The pre-defined analysis algorithm may include ICA or DVA algorithms.

[0161] In another possible implementation, the parameter correspondence is presented in the form of a curve (referred to as the first characteristic curve for ease of distinction and explanation). A preset characteristic curve segment can be obtained and matched with the first characteristic curve to determine the phase transition characteristic data corresponding to each phase transition point.

[0162] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0163] S104. Determine the health status of the battery cell based on the phase transition characteristic data corresponding to each phase transition point of the battery cell.

[0164] In one possible implementation, the reference maximum capacity of the battery cell can be determined based on the phase transition characteristic data corresponding to each phase transition point of the battery cell, and the ratio of the reference maximum capacity to the rated capacity of the battery cell can be used as the health status of the battery cell.

[0165] In another possible implementation, the reference maximum capacity of the battery cell can be determined based on the phase change characteristic data corresponding to each phase change point of the battery cell. The maximum capacity is obtained by correcting the reference maximum capacity based on a preset coefficient, and the ratio of the maximum capacity to the rated capacity of the battery cell is taken as the health status of the battery cell.

[0166] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0167] The method provided in this embodiment acquires the current and voltage value sequences of the battery cell during variable current charging. Based on these sequences, it determines the characteristic voltage sequence of the battery cell under a constant simulated current. The method then determines the parameter correspondences of the battery cell based on these characteristic voltage sequences, and further determines the phase transition characteristic data corresponding to each phase transition point based on these parameter correspondences. Finally, it determines the battery cell's health status based on the phase transition characteristic data corresponding to each phase transition point, thus providing a scheme for SOH estimation under current fluctuations during charging. Furthermore, since this embodiment can convert the changing voltage value sequence under variable current conditions into a characteristic voltage sequence under a constant simulated current, and then obtain the parameter correspondences of the battery cell based on these characteristic voltage sequences, accurate phase transition point identification can be achieved based on these parameter correspondences. Finally, accurate SOH estimation can be realized based on the phase transition characteristic data corresponding to each phase transition point.

[0168] In one embodiment, as shown in FIG2, FIG2 is a schematic flowchart of a characteristic voltage sequence determination method provided by an embodiment of this application. This embodiment relates to a possible implementation of how to determine the characteristic voltage sequence of a battery cell under a constant analog current based on the current value sequence and voltage value sequence of the battery cell. Based on the above embodiment, S102 may include the following steps S201-S204:

[0169] S201 determines multiple charging periods based on the current value sequence.

[0170] In one possible implementation, when the battery cell is in a multi-stage stepped current charging state, the current change moments in the current value sequence are obtained, and a preset time period before or after each current change moment is taken as a charging time period.

[0171] For example, taking a current value sequence in a multi-stage stepped current charging scenario as an example, this current value sequence includes two current change times: a first current change time and a second current change time. A preset period before the first current change time can be considered as a first charging period, and a preset period after the first current change time can be considered as a second charging period. A preset period before the second current change time can be considered as a third charging period, and a preset period after the second current change time can be considered as a fourth charging period.

[0172] If the current value sequence is a 600-second sequence, with the first current change occurring at the 240th second and the second current change occurring at the 420th second, the current value before the 240th second is 15 amps, the current value from the 240th second to the 420th second is 10 amps, and the current value after the 420th second is 5 amps, and the preset time period is 60 seconds, then the period between the 180th second and the 240th second can be considered as the first charging period, the period between the 240th second and the 300th second as the second charging period, the period between the 360th second and the 420th second as the third charging period, and the period between the 420th second and the 480th second as the fourth charging period.

[0173] In another possible implementation, while the battery cell is in an arbitrary variable current charging state, the current value at each time point in the current value sequence is acquired. If the difference between the current value at a time point and the current value at the previous time point is less than or equal to a first preset threshold, then that time point is considered a valid time point. A series of valid time points are then considered as a charging period. Arbitrary variable current charging largely corresponds to energy storage frequency regulation and peak shaving scenarios, where the current magnitude changes rapidly; for example, it can change multiple times within 5 seconds.

[0174] Taking a current value sequence under an arbitrary variable current charging scenario as an example, this current value sequence is a 600-second sequence. Within the time period before the 241st second, for each time point within this time period, the difference between the current value at that time point and the current value at the previous time point is less than or equal to a first preset threshold. However, the difference between the current value at the 241st second and the current value at the 240th second is greater than the first preset threshold. Therefore, each time point within the time period before the 241st second is a valid time point. Taking the first 240 seconds as a charging time period, this method can be used to determine each charging time period in the current value sequence.

[0175] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0176] S202, obtain the current value subsequence and voltage value subsequence corresponding to each charging period.

[0177] For example, taking the first charging period, the second charging period, the third charging period, and the fourth charging period as examples, the current value subsequence and voltage value subsequence corresponding to the first charging period, the current value subsequence and voltage value subsequence corresponding to the second charging period, and the current value subsequence and voltage value subsequence corresponding to the third charging period and the fourth charging period can be obtained.

[0178] S203, based on the current value subsequence and voltage value subsequence of at least two charging periods, determine the characteristic voltage subsequence of the cell under constant analog current.

[0179] In this embodiment, at least two charging periods may include adjacent charging periods or non-adjacent charging periods. Alternatively, at least two charging periods may include both adjacent and non-adjacent charging periods. For example, at least two charging periods may include a first charging period, a second charging period, and a fourth charging period, wherein the first charging period and the second charging period are adjacent charging periods, and the fourth charging period and the second charging period are non-adjacent charging periods.

[0180] For example, taking the determination of the characteristic voltage subsequence of the battery cell under constant simulated current based on the current and voltage value subsequences of the three charging periods as an example, a characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage value subsequences of the first, second, and third charging periods. For ease of subsequent explanation, this characteristic voltage subsequence will be referred to as the first characteristic voltage subsequence. Similarly, a characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage value subsequences of the second, third, and fourth charging periods. This characteristic voltage subsequence will be referred to as the second characteristic voltage subsequence. A total of two characteristic voltage subsequences are determined.

[0181] Another example is determining the characteristic voltage subsequence of the battery cell under constant simulated current based on the current and voltage subsequences of two charging periods. One characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage subsequences of the first and second charging periods; another characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage subsequences of the second and third charging periods; and yet another characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage subsequences of the third and fourth charging periods, resulting in a total of three characteristic voltage subsequences.

[0182] Because the current values ​​in the current value sequence of variable current charging vary, and the voltage values ​​in the voltage value sequence exhibit significant step changes, the ICA or DVA methods are not suitable for SOH estimation under current fluctuations during charging. Therefore, this embodiment determines the characteristic voltage subsequence of the battery cell under a constant simulated current, obtaining a characteristic voltage subsequence with voltage transitions eliminated. This allows for SOH estimation using the ICA or DVA methods based on this eliminated voltage transition characteristic voltage subsequence. Other methods can also be used for SOH estimation based on the eliminated voltage transition characteristic voltage subsequence.

[0183] S204, determine the characteristic voltage sequence based on all characteristic voltage sub-sequences of the cell under constant analog current.

[0184] A characteristic voltage sequence can be formed by splicing together all the characteristic voltage subsequences of the battery cell under a constant simulated current. For example, the first characteristic voltage subsequence and the second characteristic voltage subsequence are spliced ​​together to obtain the characteristic voltage sequence.

[0185] The method provided in this embodiment determines multiple charging periods based on current value sequences, obtains current value subsequences and voltage value subsequences corresponding to each charging period, determines the characteristic voltage subsequence of the battery cell under a constant simulated current based on the current value subsequences and voltage value subsequences of at least two charging periods, and determines the characteristic voltage sequence based on all characteristic voltage subsequences of the battery cell under a constant simulated current. The method gradually transitions the voltage value subsequences with large transitions in at least two charging periods to relatively smooth characteristic voltage subsequences, ultimately obtaining a smooth characteristic voltage sequence. Therefore, the phase transition characteristic data determined based on the characteristic voltage sequence is more accurate.

[0186] In one embodiment, based on the above embodiment, S102 above, determining the characteristic voltage sequence of the battery cell under constant analog current based on the current value sequence and voltage value sequence of the battery cell can be achieved in the following way:

[0187] Based on the current value sequence, voltage value sequence, preset charging current, and pre-trained model, a characteristic voltage sequence is obtained.

[0188] The current value sequence, voltage value sequence, and preset charging current can be input into a pre-trained model to obtain multiple characteristic voltage sub-sequences under the preset charging current. A characteristic voltage sequence is then obtained based on each characteristic voltage sub-sequence, where the preset charging current is the aforementioned constant simulated current. Alternatively, based on the scheme provided in Method 1, the characteristic voltage sub-sequence samples of the battery cell under a constant simulated current sample can be determined using the current value sequence samples and voltage value sequence samples of the battery cell. That is, the method for obtaining the characteristic voltage sub-sequence samples is the same as in Method 1. For ease of explanation, a current value sequence sample and its corresponding voltage value sequence sample are considered as a sample pair. One sample pair can correspond to multiple characteristic voltage sub-sequence samples under a constant simulated current sample. The multiple characteristic voltage sub-sequence samples corresponding to a sample pair are concatenated to obtain the characteristic voltage sequence sample.

[0189] In one possible implementation, an initial model can be trained based on multiple sample pairs and feature voltage subsequence sample pairs under each sample pair to obtain a pre-trained model; the pre-trained model is used to output multiple feature voltage subsequences based on current value sequence, voltage value sequence, and preset charging current, and then a feature voltage sequence is obtained based on multiple feature voltage subsequences.

[0190] In another possible implementation, a pre-trained model can be obtained by training an initial model based on multiple sample pairs and the feature voltage sequence sample pairs under each sample pair. This pre-trained model is used to directly output the feature voltage sequence based on the input current value sequence, voltage value sequence, and preset charging current.

[0191] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0192] The method provided in this embodiment obtains a characteristic voltage sequence based on the current value sequence, voltage value sequence, preset charging current, and pre-trained model. It transitions the voltage value sequence with large transitions to a relatively smooth characteristic voltage sequence, thereby making the health status determined based on the characteristic voltage sequence more accurate.

[0193] In one embodiment, the characteristic voltage sequence obtained from the current value sequence, voltage value sequence, preset charging current, and pre-trained model can be achieved as follows:

[0194] The current value sequence, voltage value sequence, and preset charging current are input into the pre-trained model to obtain multiple characteristic voltage sub-sequences under the preset charging current; the characteristic voltage sequence is obtained based on each characteristic voltage sub-sequence.

[0195] The method provided in this embodiment obtains multiple characteristic voltage subsequences under the preset charging current by inputting a current value sequence, a voltage value sequence, and a preset charging current into a pre-trained model, and then obtains a characteristic voltage sequence based on each characteristic voltage subsequence. Since multiple characteristic voltage subsequences can be directly obtained through the pre-trained model, the efficiency of obtaining multiple characteristic voltage subsequences is improved, thereby improving the efficiency of obtaining the characteristic voltage sequence.

[0196] In one embodiment, S203 above, determining the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of at least two charging periods, can be achieved in the following manner:

[0197] Based on the current and voltage subsequences of any multiple adjacent charging periods, the characteristic voltage subsequence of the battery cell under constant simulated current is determined.

[0198] Referring to the above example, taking the determination of the characteristic voltage subsequence of the battery cell under constant simulated current based on the current and voltage value subsequences of the three charging periods as an example, if the current value sequence includes the first, second, third, and fourth charging periods of the example above, a first characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage value subsequences of the first, second, and third charging periods; and another second characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage value subsequences of the second, third, and fourth charging periods. A total of two characteristic voltage subsequences are determined.

[0199] Alternatively, taking the determination of the characteristic voltage subsequence of the battery cell under constant simulated current based on the current and voltage subsequences of two charging periods as an example, one characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage subsequences of the first and second charging periods; another characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage subsequences of the second and third charging periods; and yet another characteristic voltage subsequence of the battery cell under constant simulated current can be determined based on the current and voltage subsequences of the third and fourth charging periods, resulting in a total of three characteristic voltage subsequences.

[0200] The method provided in this embodiment can determine the characteristic voltage subsequence of the battery cell under constant simulated current based on the current value subsequence and voltage value subsequence of any adjacent multiple charging periods. It can capture the current value subsequence and voltage value subsequence before or after multiple consecutive current change moments, and is more adaptable to the internal mechanism changes of the battery cell during the corresponding charging period, thereby obtaining a relatively smooth transition characteristic voltage subsequence.

[0201] In one embodiment, S203 above, determining the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of at least two charging periods, can be achieved in the following manner:

[0202] Based on the current and voltage subsequences of any two adjacent charging periods in multiple charging periods, the characteristic voltage subsequence of the battery cell under constant simulated current is determined.

[0203] Based on the above example, if the current value sequence A includes a first charging period, a second charging period, a third charging period, and a fourth charging period, a characteristic voltage subsequence of the battery cell under constant simulated current is determined based on the current value subsequence and voltage value subsequence of the first and second charging periods; and a characteristic voltage subsequence of the battery cell under constant simulated current is determined based on the current value subsequence and voltage value subsequence of the third and fourth charging periods, for a total of two characteristic voltage subsequences.

[0204] The method provided in this embodiment determines the characteristic voltage subsequence of the battery cell under constant simulated current by using the current value subsequence and voltage value subsequence of any two adjacent charging periods in multiple charging periods. Since one characteristic voltage subsequence corresponds to only two adjacent charging periods, any two adjacent charging periods can accurately capture the current value subsequence and voltage value subsequence before or after the current change moment. Moreover, the current change time of two adjacent charging periods is short, and the internal mechanism change of the battery cell is relatively small. Thus, based on the current value subsequence and voltage value subsequence of two adjacent charging periods, several accurate and smoothly transitioning characteristic voltage subsequences can be obtained.

[0205] In one embodiment, as shown in FIG3, FIG3 is a flowchart illustrating a method for determining multiple charging periods according to an embodiment of this application. This embodiment relates to a possible implementation of how to determine multiple charging periods based on a current value sequence. Based on the above embodiment, S201 may include the following steps S301-S302:

[0206] S301, when the battery cell is in the working state of multi-stage stepped current charging, acquires the current change moments in the current value sequence.

[0207] S302 defines a preset period of time before or after each current fluctuation as a charging period.

[0208] For example, taking a current value sequence in a multi-stage stepped current charging scenario as an example, the current value sequence includes two current change moments: a first current change moment and a second current change moment. A preset period before the first current change moment can be considered as a first charging period, and a preset period after the first current change moment can be considered as a second charging period. A preset period before the second current change moment can be considered as a third charging period, and a preset period after the second current change moment can be considered as a fourth charging period.

[0209] The method provided in this embodiment obtains each current change moment in the current value sequence when the battery cell is in a multi-stage stepped current charging state, and takes the preset time period before or after each current change moment as a charging time period, so that the obtained charging time period is more in line with the actual stepped current charging scenario. Moreover, the determination of the current change moment is relatively simple and quick. Therefore, the efficiency of obtaining the charging time period can be improved by determining the charging time period through the current change moment.

[0210] In one embodiment, as shown in FIG4, FIG4 is a flowchart illustrating another method for determining multiple charging periods provided by an embodiment of this application. This embodiment relates to a possible implementation of how to determine multiple charging periods based on a current value sequence. Based on the above embodiment, S201 may include the following steps S401-S403:

[0211] S401 acquires the current value at each time point in the current value sequence when the battery cell is in a working state of arbitrary current charging.

[0212] S402, if the difference between the current value at a time point and the current value at the previous time point is less than or equal to the first preset threshold, then the time point is taken as a valid time point.

[0213] S403 uses consecutive valid time points as a charging period.

[0214] The method provided in this embodiment acquires the current values ​​at various time points in a current value sequence when the battery cell is in an arbitrary variable current charging state. If the difference between the current value at a time point and the current value at the previous time point is less than or equal to a first preset threshold, then that time point is considered a valid time point. Consecutive valid time points are considered as a charging period, making the obtained charging period more consistent with the actual arbitrary variable current charging scenario. Furthermore, this method of using a time point as a valid time point when the difference between the current value at a time point and the current value at the previous time point is less than or equal to the first preset threshold is relatively simple and fast, thus increasing the probability of obtaining valid time points and consequently increasing the probability of obtaining a charging period.

[0215] In one embodiment, as shown in Figure 5, which is a flowchart illustrating a method for determining a characteristic voltage subsequence according to an embodiment of this application, this embodiment relates to a possible implementation of determining the characteristic voltage subsequence of a battery cell under a constant simulated current based on the current value subsequence and voltage value subsequence of any two adjacent charging periods in multiple charging periods. Based on the above embodiment, the method may include the following steps S501-S503:

[0216] S501, determine the total charging capacity corresponding to at least two charging periods based on the current value subsequence of at least two charging periods among multiple charging periods.

[0217] The total charging capacity corresponding to at least two charging periods can be obtained by integrating the current value subsequences of at least two charging periods.

[0218] Taking the determination of the total charging capacity corresponding to two charging periods based on current value subsequences from multiple charging periods as an example, the two current value subsequences are a first current value subsequence and a second current value subsequence. The first current value subsequence is a 30-second sequence with a current value of 10 amps, meaning the current value from the 1st second to the 30th second is 10A. The second current value subsequence is also a 30-second sequence with a current value of 5 amps, meaning the current value from the 31st second to the 60th second is 5 amps. The charging capacity of the first current value subsequence within 30 seconds can be determined using the ampere-hour integration method, which identifies it as 300 amps. Similarly, the charging capacity of the second current value subsequence within 30 seconds can be calculated as 150 amps. Therefore, the total charging capacity corresponding to the two current value subsequences is 450 amps.

[0219] Similar to the above method of determining the total charging capacity corresponding to two charging periods based on the current value subsequences of two charging periods among multiple charging periods, the total charging capacity corresponding to at least two charging periods can be determined based on the current value subsequences of three or more charging periods among multiple charging periods. This will not be elaborated further here.

[0220] S502, based on the total charging capacity corresponding to at least two charging periods, determine the characteristic current subsequence of the cell under constant analog current.

[0221] Based on the above example, with a constant analog current of 6 amps, a characteristic current subsequence with a duration of 75 seconds can be determined based on the total charging capacity and the constant analog current. That is, with the total charging capacity remaining unchanged, a characteristic current subsequence is determined based on the preset charging current.

[0222] It should be noted that the characteristic current subsequence is the desired excitation curve. The above example only uses a 6A characteristic current subsequence with a duration of 75 seconds. The preset charging current can be between the current value of the first current value subsequence and the current value of the second current value subsequence, or it can be outside the boundary between the two. The specific value can be selected according to actual needs.

[0223] S503, based on the current value subsequence and voltage value subsequence of at least two charging periods, and the characteristic current subsequence of the cell under constant analog current, determine the characteristic voltage subsequence of the cell under constant analog current.

[0224] The characteristic voltage subsequence of the battery cell under constant analog current can be determined as follows:

[0225] Method 1: Input the current and voltage subsequences of the two charging periods into the battery model to calibrate the parameters of the battery model and obtain the target battery model after parameter calibration; input the characteristic current subsequence of the cell under constant simulated current into the target battery model to obtain the characteristic voltage subsequence of the cell under constant simulated current.

[0226] Method 2: The current and voltage subsequences of the two charging periods, as well as the characteristic current subsequence of the cell under constant simulated current, can be input into the voltage subsequence prediction model to obtain the characteristic voltage subsequence of the cell under constant simulated current.

[0227] Based on the scheme provided in Method 1 above, the characteristic voltage subsequence samples of the battery cell under constant simulated current can be determined using current and voltage subsequence samples from two charging periods of the battery cell, as well as the characteristic current subsequence samples of the battery cell under constant simulated current. The current and voltage subsequence samples from the two charging periods, along with the characteristic current and voltage subsequence samples of the battery cell under constant simulated current, are used as training data. Multiple training data sets can be obtained, and an initial voltage subsequence prediction model is trained based on these multiple training data sets to obtain the voltage subsequence prediction model. Then, the current and voltage subsequence samples from the two charging periods, along with the characteristic current subsequence of the battery cell under constant simulated current, are input into the voltage subsequence prediction model to obtain the characteristic voltage subsequence of the battery cell under constant simulated current.

[0228] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0229] The method provided in this embodiment determines the total charging capacity corresponding to at least two charging periods based on the current value subsequences of at least two charging periods. Based on the total charging capacity corresponding to the at least two charging periods, it determines the characteristic current subsequence of the battery cell under a constant simulated current. Furthermore, based on the current and voltage value subsequences of the at least two charging periods, and the characteristic current subsequence of the battery cell under a constant simulated current, it determines the characteristic voltage subsequence of the battery cell under a constant simulated current. By converting the current and voltage value subsequences under varying current conditions into characteristic voltage subsequences under simulated current conditions, the method eliminates the problem of voltage jumps in the curve of voltage value changing with charging capacity under varying current conditions.

[0230] In one embodiment, as shown in FIG6, FIG6 is a flowchart illustrating another method for determining a characteristic voltage subsequence provided in this application embodiment. This embodiment relates to a possible implementation of how to determine the characteristic voltage subsequence of a battery cell under a constant analog current based on the current value subsequence and voltage value subsequence of at least two charging periods, and the characteristic current subsequence of the battery cell under a constant analog current. Based on the above embodiment, S503 may include the following steps S601-S602:

[0231] S601, input the current value subsequence and voltage value subsequence of at least two charging periods into the battery model to calibrate the parameters of the battery model and obtain the target battery model after parameter calibration.

[0232] Referring to Figure 7, which is a schematic diagram of a variable current operating condition provided in an embodiment of this application, the battery cell experiences current fluctuations during charging. For example, Figure 7 shows a stepped current variation. Figure 7 illustrates two adjacent different current value subsequences, namely current value subsequence A1 and current value subsequence A2, which exhibit stepped variations. The original measurement data includes current value subsequences A1 and A2, as well as the voltage value subsequences corresponding to current value subsequences A1 and A2. For example, current value subsequence A1 is a 30-second sequence with a current value of 10 amps, meaning the current value from the 1st second to the 30th second is 10 A. Current value subsequence A2 is also a 30-second sequence with a current value of 5 amps, meaning the current value from the 31st second to the 60th second is 5 amps. The process involves first charging the battery cell with current value subsequence A1 for 30 seconds, followed by charging it with current value subsequence A2 for another 30 seconds. The corresponding voltage subsequences for these two current value subsequences are shown in Figure 7. These two current value subsequences and their corresponding voltage value subsequences are used as raw measurement data and input into the battery model to calibrate its parameters. For ease of explanation, this parameter calibration will be referred to as battery model calibration. It should be noted that before calibration, the battery model's parameters are unknown. By inputting the raw measurement data into the battery model, the parameters can be determined, thus completing the calibration. After the battery model's parameters are determined, this model is used as the target battery model.

[0233] S602, input the characteristic current subsequence of the cell under constant simulated current into the target battery model to obtain the characteristic voltage subsequence of the cell under constant simulated current.

[0234] Based on the exemplary descriptions in the above steps, the charging capacity of the current value subsequence A1 within 30 seconds can be determined using the ampere-hour integration method, which determines that the charging capacity within 30 seconds is 300 ampere-seconds. Similarly, the charging capacity of the current value subsequence A2 within 30 seconds can be calculated as 150 ampere-seconds, meaning the total charging capacity corresponding to the two current value subsequences A1 and A2 is 450 ampere-seconds. Assuming the preset charging current is 6 amps, a characteristic current subsequence with a duration of 75 seconds can be determined based on this total charging capacity and the constant analog current. That is, with the total charging capacity remaining unchanged, a characteristic current subsequence is determined based on the preset charging current, thereby transforming the two adjacent different current value subsequences A1 and A2, which exhibit step-changing current values, into a constant characteristic current subsequence, as shown in Figure 7.

[0235] After being transformed into a characteristic current subsequence, as shown in Figure 7, the characteristic current subsequence is input into the target battery model in S101. The target battery model can generate a characteristic voltage subsequence as shown in Figure 7. The duration of the characteristic voltage subsequence is consistent with the duration of the characteristic current subsequence, that is, the characteristic voltage subsequence is aligned with the characteristic current subsequence in time. At the same time point, there is a characteristic voltage value in the characteristic voltage subsequence and a characteristic current value in the characteristic current subsequence.

[0236] It should be noted that the battery model in this embodiment can be calibrated once or multiple times. The number of calibrations of the battery model is related to the number of charging periods and the number of charging periods corresponding to the current value subsequence and voltage value subsequence input in S601.

[0237] For ease of explanation, at least two charging periods in S601 are considered as a charging period group. If the current value sequence corresponds to four charging periods, and S601 uses two charging periods as a charging period group, then the current value sequence corresponds to three charging period groups. For each charging period group, the battery model needs to be recalibrated once.

[0238] For example, the target battery model shown in Figure 7 above is obtained by calibrating the parameters of the battery model based on the current value subsequence and voltage value subsequence under the first charging period group. The characteristic voltage subsequence under the first charging period group is obtained using this target battery model. Similarly, the parameters of the battery model need to be calibrated based on the current value subsequence and voltage value subsequence under the second charging period group to obtain a new target battery model. The characteristic current subsequence corresponding to the second charging period is then input into this target battery model to obtain the characteristic voltage subsequence under the second charging period group. And the parameters of the battery model need to be calibrated based on the current value subsequence and voltage value subsequence under the third charging period group to obtain a new target battery model. The characteristic current subsequence corresponding to the third charging period is then input into this target battery model to obtain the characteristic voltage subsequence under the third charging period group.

[0239] The method provided in this embodiment calibrates the parameters of a battery model by inputting current and voltage subsequences from at least two charging periods into the model, resulting in a calibrated target battery model. The characteristic current subsequence of the battery cell under constant simulated current is then input into the target battery model to obtain a characteristic voltage subsequence of the battery cell under constant simulated current. The battery model is calibrated online based on the current and voltage subsequences from at least two charging periods. The characteristic voltage subsequence is then determined using the online-calibrated target battery model and the characteristic current subsequence obtained from the current subsequences of at least two charging periods, making the characteristic voltage subsequence more consistent with the actual charging scenario of the battery cell and resulting in a smoother transition.

[0240] In one embodiment, as shown in FIG8, FIG8 is one of the flowcharts of the method for determining phase change characteristic data provided in this application embodiment. This embodiment relates to a possible implementation of how to determine the phase change characteristic data corresponding to each phase change point of the battery cell according to the parameter correspondence relationship. In some embodiments, the parameter correspondence relationship can be presented in the form of a first characteristic curve. The "determining the phase change characteristic data corresponding to each phase change point of the battery cell according to the parameter correspondence relationship" in S103 above may include the following steps S801-S802:

[0241] S801, Obtain the preset feature curve segment.

[0242] S802, Match the preset characteristic curve segment with the first characteristic curve to determine the phase transition characteristic data corresponding to each phase transition point.

[0243] In one possible implementation, a first characteristic sub-curve can be determined based on the characteristic voltage sub-sequence and the state of charge (SOC) sub-sequence, and a first characteristic curve can be obtained based on each first characteristic sub-curve. The first characteristic curve can be used to characterize the correspondence between the characteristic voltage value and the SOC; for example, the first characteristic curve is a curve with SOC on the horizontal axis and the characteristic voltage value on the vertical axis.

[0244] The aforementioned SOC subsequence can be achieved through the following process: For example, during the charging process of the battery cell using the first current value subsequence and the second current value subsequence, an initial SOC subsequence lasting 60 seconds during overcharging can be obtained. This initial SOC subsequence is then transformed into an SOC subsequence with a duration consistent with the characteristic current subsequence. That is, the characteristic current subsequence is time-aligned with the SOC subsequence, and the same time point corresponds to a constant analog current in the characteristic current subsequence and a SOC in the SOC subsequence. Referring to the example above, if the characteristic current subsequence is the aforementioned 75-second duration, then the initial SOC subsequence is transformed into a 75-second duration SOC subsequence, achieving time alignment between the obtained SOC subsequence and the characteristic current subsequence. Since the characteristic current subsequence is time-aligned with the characteristic voltage subsequence, the SOC subsequence is also time-aligned with the characteristic voltage subsequence. Therefore, based on the characteristic voltage subsequence and the time-aligned SOC subsequence, the first characteristic sub-curve is determined. It should be noted that the same time point corresponds to a constant analog current in the characteristic current subsequence, a characteristic voltage value in the characteristic voltage subsequence, and a SOC in the SOC subsequence. The SOC subsequence is a sequence that includes the correspondence between a series of SOCs and time points.

[0245] In another possible implementation, a first characteristic sub-curve can be determined based on the characteristic voltage sub-sequence and the charging capacity sub-sequence, and a first characteristic curve can be obtained based on each first characteristic sub-curve. The first characteristic curve can be used to characterize the correspondence between the characteristic voltage value and the charging capacity; for example, the first characteristic curve is a curve where the charging capacity is on the horizontal axis and the characteristic voltage value is on the vertical axis.

[0246] The aforementioned charging capacity subsequence can be achieved through the following process: For example, referring to the above example, the characteristic current subsequence is the 75-second characteristic current subsequence. Based on the characteristic current subsequence, the charging capacity corresponding to each time point within the 75-second duration can be determined using the ampere-hour integration method. The time points within the 75-second duration and their corresponding charging capacities form a 75-second charging capacity subsequence. This ensures that the obtained charging capacity subsequence is time-aligned with the characteristic voltage subsequence and the characteristic current subsequence. Then, based on the characteristic voltage subsequence and the time-aligned charging capacity subsequence, a first characteristic sub-curve is determined. This first characteristic sub-curve characterizes the correspondence between the characteristic voltage value and the charging capacity. It should be noted that the same time point corresponds to a constant simulated current in the characteristic current subsequence, a characteristic voltage value in the characteristic voltage subsequence, and a charging capacity in the charging capacity subsequence. The charging capacity subsequence is a sequence that includes a series of correspondences between charging capacity and time points.

[0247] Similar to obtaining the first characteristic curve described above, an open-circuit voltage subsequence can be obtained based on the characteristic voltage subsequence. Based on the open-circuit voltage subsequence and the State of Charge (SOC) subsequence, a first characteristic sub-curve can be determined, and the first characteristic curve can be obtained based on each of the first characteristic sub-curves. The first characteristic curve can be used to characterize the correspondence between the open-circuit voltage value and the SOC; for example, the first characteristic curve is a curve with SOC on the horizontal axis and the open-circuit voltage value on the vertical axis. Alternatively, an open-circuit voltage subsequence can be obtained based on the characteristic voltage subsequence. Based on the open-circuit voltage subsequence and the charging capacity subsequence, a first characteristic sub-curve can be determined, and the first characteristic curve can be obtained based on each of the first characteristic sub-curves. The first characteristic curve can also be used to characterize the correspondence between the open-circuit voltage value and the charging capacity; for example, the first characteristic curve is a curve with the charging capacity on the horizontal axis and the open-circuit voltage value on the vertical axis.

[0248] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0249] As shown in Figure 9, Figure 9 is a schematic diagram of a first characteristic curve and three characteristic curve segments provided in an embodiment of this application. In Figure 9, the first characteristic curve is used to characterize the correspondence between characteristic voltage values ​​and SOC. In Figure 9, the vertical axis represents the voltage value, and the unit is volts. For the first characteristic curve, the vertical axis represents the voltage value on the first characteristic curve; for the characteristic curve segments, the vertical axis represents the phase transition voltage value.

[0250] Assume that the independent variable of the first characteristic curve can be a list of SOCs {SOC1, SOC2, SOC3, ..., SOCN}, and the dependent variable can be a list of characteristic voltages of the battery cell {volt_simu_1, volt_simu_2, volt_simu_3, ..., volt_simu_N}; assume that the independent variable of characteristic curve segment 1 can be a list of preset SOCs {SOCpreset1,1, SOCpreset1,2, SOCpreset1,3, ..., SOCpreset1,m} and the dependent variable is a list of phase change voltages {Voltp1,1, Voltp1,2, Voltp1,3, ..., Voltp1,m}; assume that characteristic curve segment The independent variable of characteristic curve segment 2 can be a preset SOC list {SOCpreset2,1,SOCpreset2,2,SOCpreset2,3,…,SOCpreset2,n} and the dependent variable is a phase transition voltage list {Voltp2,1,Voltp2,2,Voltp2,3,…,Voltp2,n}; assuming that the independent variable of characteristic curve segment 3 can be a preset SOC list {SOCpreset3,1,SOCpreset3,2,SOCpreset3,3,…,SOCpreset3,q} and the dependent variable is a phase transition voltage list {Voltp3,1,Voltp3,2,Voltp3,3,…,Voltp3,q}.

[0251] The process of matching a preset feature curve segment with a first feature curve to determine the phase transition characteristic data corresponding to each phase transition point can be achieved in the following way:

[0252] If the first characteristic curve is used to characterize the correspondence between the characteristic voltage value and the state of charge (SOC), for example, the first characteristic curve is a curve with SOC on the horizontal axis and the characteristic voltage value on the vertical axis, then the characteristic curve segment is used to characterize the correspondence between the phase transition voltage value and the preset state of charge. The horizontal axis of the characteristic curve segment is the preset state of charge, and the vertical axis is the phase transition voltage.

[0253] In one possible implementation, for each feature curve segment, the first feature curve can be divided into multiple first candidate curve segments based on that feature curve segment. The first candidate curve segment with the highest similarity to the feature curve segment is then determined, and this first candidate curve segment with the highest similarity to the feature curve segment is taken as the target candidate curve segment that matches the feature curve segment. In this way, the target candidate curve segments that match each feature curve segment can be determined.

[0254] In another possible implementation, for each feature curve segment, the first feature curve can be divided into multiple first candidate curve segments based on that feature curve segment. The first candidate curve segment with the smallest difference from the feature curve segment is then determined, and this first candidate curve segment with the smallest difference from the feature curve segment is taken as the target candidate curve segment that matches the feature curve segment. In this way, the target candidate curve segment that matches each feature curve segment can be determined.

[0255] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0256] In one embodiment, if the first characteristic curve is used to characterize the correspondence between the characteristic voltage value and the charging capacity, for example, if the first characteristic curve is a curve with the charging capacity on the horizontal axis and the characteristic voltage value on the vertical axis, then the characteristic curve segment is used to characterize the correspondence between the phase transition voltage value and the preset charging capacity, with the preset charging capacity on the horizontal axis and the phase transition voltage on the vertical axis of the characteristic curve segment. Similar to the method described above for determining the target candidate curve segments to match each characteristic curve segment, the target candidate curve segments to match the characteristic curve segments used to characterize the correspondence between the phase transition voltage value and the preset charging capacity can be determined.

[0257] After the target candidate curve segments for matching each characteristic curve segment are determined, each phase transition point can be determined from the target candidate curve segments according to the phase transition point location determination strategy, thereby determining the phase transition characteristic data of each phase transition point. The phase transition characteristic data of a phase transition point includes the state of charge and charging capacity at that phase transition point. One target candidate curve segment can determine one phase transition point. The starting data point, ending data point, intermediate data point, or other data points in the target candidate curve segment can all be used as phase transition points. It should be noted that the same phase transition point location determination strategy must be used to determine the phase transition points on all target candidate curve segments.

[0258] The method provided in this embodiment obtains a preset feature curve segment and matches the preset feature curve segment with a first feature curve to determine the phase transition feature data corresponding to each phase transition point. Since the preset feature curve segment can accurately reflect the phase transition point, the phase transition feature data corresponding to the phase transition point obtained based on the preset feature curve segment is more accurate.

[0259] In one embodiment, as shown in FIG10, FIG10 is a second schematic flowchart of the phase transition feature data determination method provided in this application embodiment. This embodiment relates to a possible implementation of how to match a preset feature curve segment with a first feature curve to determine the phase transition feature data corresponding to each phase transition point. Exemplarily, the above-mentioned S802 may include the following steps S1001-S1002:

[0260] S1001, determine the corresponding rectangular window based on each preset feature curve segment.

[0261] A curve showing the relationship between voltage and SOC can be plotted based on test data generated during the charging process of multiple sample cells, with voltage on the ordinate and SOC on the abscissa. The abrupt changes in the slope of the curve are analyzed, and these abrupt changes are taken as target locations. Since these target locations are usually phase transition points where the cell mechanism changes, characteristic curve segments can be pre-created based on these target locations. The phase transition points of the cell can then be determined using these characteristic curve segments. For example, if charging begins when the SOC of a sample cell is low, such as close to 0, the first slope in the curve segment before the cell reaches 15% SOC is relatively large. As charging continues, the second slope in the range of 15% SOC to 50% SOC is significantly smaller than the first slope. A sudden change in slope occurs again at 50% SOC and at 90% SOC. These 15%, 50%, and 90% SOC locations can be considered as abrupt change points. The difference between the current slope and the previous slope can be compared; if the difference is greater than a preset difference, a sudden change in slope can be identified.

[0262] For each preset feature curve segment, the corresponding rectangle can be determined based on the length of the feature curve segment in the horizontal axis and the length in the vertical axis. Taking feature curve segment 1 shown in Figure 9 as an example, the length of feature curve segment 1 in the horizontal axis can be used as the length of the first rectangle in the horizontal axis, and the length of feature curve segment 1 in the vertical axis can be used as the length of the first rectangle in the vertical axis, thereby determining the first rectangle. Alternatively, the length of feature curve segment 1 in the horizontal axis can be used as the length of the first rectangle in the horizontal axis, and the result of multiplying the length of feature curve segment 1 in the vertical axis by a coefficient close to 1 can be used as the length of the first rectangle in the vertical axis, thereby determining the first rectangle.

[0263] Following the method for determining the first rectangle, the rectangle corresponding to feature curve segment 2 and the rectangle corresponding to feature curve segment 3 can be determined.

[0264] S1002, based on each rectangular window and the first preset number of segments, the first feature curve is divided into multiple first candidate curve segments along the horizontal axis.

[0265] Based on the first preset number of segments, the movement step size of the rectangular window can be determined. Based on the movement step size, the rectangular window is moved along the first feature curve in the horizontal direction to obtain multiple first candidate curve segments. For example, taking the movement of the first rectangular frame along the first feature curve in the horizontal direction as an example, before the movement, the leftmost end of the first rectangular frame is aligned with the leftmost end of the first feature curve in the horizontal direction, and the segment on the first feature curve selected by the first rectangular frame is taken as the first candidate curve segment; then, the first rectangular frame is moved to the right in the horizontal direction, and the movement distance is equal to the movement step size, and the segment on the first feature curve selected by the area where the first rectangular frame is located at this time is taken as the second candidate curve segment; then, the first rectangular frame continues to be moved to the right in the horizontal direction, and the movement distance is equal to the movement step size, and the segment on the first feature curve selected by the area where the first rectangular frame is located at this time is taken as the third candidate curve segment, and so on, so that the first feature curve can be divided into multiple first candidate curve segments along the horizontal direction.

[0266] S1003, determine the target candidate curve segment that matches the preset feature curve segment from multiple first candidate curve segments.

[0267] In one possible implementation: For each feature curve segment, the first feature curve can be divided into multiple first candidate curve segments based on that feature curve segment. The first candidate curve segment with the highest similarity to the feature curve segment is determined, and this first candidate curve segment with the highest similarity to the feature curve segment is taken as the target candidate curve segment that matches the feature curve segment. In this way, the target candidate curve segments that match each feature curve segment can be determined.

[0268] In another possible implementation: For each feature curve segment, the first feature curve can be divided into multiple first candidate curve segments based on that feature curve segment. The first candidate curve segment with the smallest difference from the feature curve segment is then determined, and this first candidate curve segment with the smallest difference from the feature curve segment is taken as the target candidate curve segment that matches the feature curve segment. In this way, the target candidate curve segments that match each feature curve segment can be determined.

[0269] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0270] S1004, determine the phase transition characteristic data corresponding to each phase transition point based on each target candidate curve segment.

[0271] The phase transition characteristic data corresponding to the phase transition point can include the charging capacity and SOC corresponding to the phase transition point.

[0272] In one possible implementation, if the first characteristic curve is used to characterize the correspondence between the characteristic voltage value and the State of Charge (SOC), it is assumed that the first characteristic curve is divided into three candidate curve segments: A, B, and C. Candidate curve segment A is the target candidate curve segment that matches characteristic curve segment A. The intermediate data point on this target candidate curve segment is taken as the phase transition point, and the SOC on the horizontal axis of Figure 9 corresponding to this phase transition point is taken as the SOC in the phase transition characteristic data of that phase transition point. After determining the SOC in the phase transition characteristic data of this phase transition point, the time point of this SOC in the SOC sequence can be determined. This time point is the phase transition time point corresponding to the phase transition point. Then, the charging capacity corresponding to this time point is determined in the charging capacity sequence, and this charging capacity is taken as the charging capacity of the phase transition point. Once the SOC and charging capacity in the phase transition characteristic data of this phase transition point are determined, the phase transition characteristic data corresponding to the phase transition point is also determined. The SOC sequence is obtained based on the SOC subsequence mentioned in the above embodiment, and the charging capacity sequence is obtained based on the charging capacity subsequence mentioned above.

[0273] In another possible implementation, where the first characteristic curve is used to characterize the correspondence between characteristic voltage values ​​and charging capacity, it is assumed that the first characteristic curve is divided into three candidate curve segments: A, B, and C. Candidate curve segment A is the target candidate curve segment that matches characteristic curve segment A. The intermediate data point on this target candidate curve segment is taken as the phase transition point, and the charging capacity on the horizontal axis corresponding to this phase transition point is taken as the charging capacity in the phase transition characteristic data of that phase transition point. Once the charging capacity in the phase transition characteristic data of this phase transition point is determined, the time point of this charging capacity in the charging capacity sequence can be determined. This time point is the phase transition time point corresponding to the phase transition point. Then, the SOC corresponding to this time point is determined in the SOC sequence, and this SOC is taken as the SOC in the phase transition characteristic data of that phase transition point. Once the SOC and charging capacity in the phase transition characteristic data of this phase transition point are determined, the phase transition characteristic data corresponding to the phase transition point is also determined.

[0274] Phase transition points can be determined from target candidate curve segments based on a phase transition point location determination strategy, thereby identifying the phase transition characteristic data for each point. The phase transition characteristic data for a single phase transition point includes the state of charge and charging capacity at that point. One target candidate curve segment can identify one phase transition point. The phase transition point can be the starting data point, the ending data point, the middle data point, or any other data point within the target candidate curve segment. It is important to note that the same phase transition point location determination strategy must be used to determine the phase transition points on all target candidate curve segments.

[0275] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0276] The method provided in this embodiment determines corresponding rectangular windows based on each preset feature curve segment. Based on each rectangular window and a first preset number of segments, the first feature curve is divided into multiple first candidate curve segments along the horizontal axis. From these multiple first candidate curve segments, target candidate curve segments that match the preset feature curve segments are determined. Phase transition feature data corresponding to each phase transition point is determined based on each target candidate curve segment. By dividing the first feature curve into finer-grained first candidate curve segments, and by gradually matching each fine-grained first candidate curve segment with the feature curve segment, the target candidate curve segments can be determined more accurately and quickly, thereby determining the phase transition feature data more accurately and quickly.

[0277] In one embodiment, referring to FIG11, FIG11 is a schematic flowchart of a method for determining a target candidate curve segment according to an embodiment of the present application. This embodiment relates to a possible implementation of how to determine a target candidate curve segment that matches a preset feature curve segment from a plurality of first candidate curve segments. Based on the above embodiment, S1003 may include the following steps S1101-S1102.

[0278] S1101, for each first candidate curve segment, the degree of matching between the curve features of the first candidate curve segment and the curve features of the preset characteristic curve segment is determined based on the characteristic voltage value of the first data point on the first candidate curve segment and the phase transition voltage value of the second data point on the preset characteristic curve segment; the first data point corresponds to the second data point.

[0279] S1102, based on the matching degree of each first candidate curve segment, determine the target candidate curve segment that matches the preset feature curve segment from multiple first candidate curve segments.

[0280] The following embodiments of this application use the example of obtaining the target candidate curve segment corresponding to feature curve segment 1 from the first feature curve for illustration.

[0281] In some embodiments, for each first candidate curve segment, a voltage difference parameter can be obtained between the characteristic voltage value corresponding to the first data point on the first candidate curve segment and the phase transition voltage value corresponding to the second data point on the characteristic curve segment. The degree of difference or matching between the curve features of the first candidate curve segment and the curve features of the characteristic curve segment is determined based on the voltage difference parameter. The voltage difference parameter can be used to characterize the degree of difference or matching between the curve features of the first candidate curve segment and the curve features of the characteristic curve segment.

[0282] For example, the degree of matching between the curve features of the first candidate curve segment and the curve features of the second feature region can be determined based on the voltage difference parameter between the characteristic voltage value corresponding to the first data point on the first candidate curve segment and the phase transition voltage value corresponding to the second data point on the feature curve segment. The voltage difference parameter is negatively correlated with the degree of matching. For instance, the smaller the voltage difference parameter between the characteristic voltage value corresponding to the first data point on the first candidate curve segment and the phase transition voltage value corresponding to the second data point on the feature curve segment, the higher the degree of matching between the curve features of the first candidate curve segment and the curve features of the second feature region; conversely, the larger the voltage difference parameter between the characteristic voltage value corresponding to the first data point on the first candidate curve segment and the phase transition voltage value corresponding to the second data point on the feature curve segment, the lower the degree of matching between the curve features of the first candidate curve segment and the curve features of the second feature region.

[0283] Alternatively, the voltage difference parameter is positively correlated with the degree of difference. For example, the smaller the voltage difference parameter between the characteristic voltage value corresponding to the first data point on the first candidate curve segment and the phase transition voltage value corresponding to the second data point on the characteristic curve segment, the smaller the degree of difference between the curve features of the first candidate curve segment and the curve features of the second characteristic region; conversely, the larger the voltage difference parameter between the characteristic voltage value corresponding to the first data point on the first candidate curve segment and the phase transition voltage value corresponding to the second data point on the characteristic curve segment, the greater the degree of difference between the curve features of the first candidate curve segment and the curve features of the second characteristic region.

[0284] The voltage difference parameter between the curve characteristics of each first candidate curve segment and the curve characteristics of the characteristic curve segment can be determined by the following formula (1):

[0285]

[0286] Diff represents the voltage difference parameter; Volt interp,k Volt represents the characteristic voltage value of the k-th first data point on the first candidate curve segment. p,k p represents the phase transition voltage value at the k-th second data point on the characteristic curve segment. 0 p represents the first preset coefficient; 1 represents the second preset coefficient, and r represents the total number of data points on the feature curve segment. Specifically, the first data point corresponding to the minimum abscissa value on the first candidate curve segment can be used as the first first data point on the candidate segment, and the second data point corresponding to the minimum abscissa value on the feature curve segment can be used as the first second data point.

[0287] It should be noted that the voltage difference coefficient can also be calculated based on a modified formula of the above formula (1). For example, the voltage difference coefficient can be calculated using a modified formula of the above formula (1). The result is used as the voltage difference parameter.

[0288] For example, for the aforementioned characteristic curve segment 1, which has m data points, the voltage difference parameter between the curve characteristics of each first candidate curve segment and the curve characteristics of characteristic curve segment 1 can be determined by the following formula (2):

[0289]

[0290] Among them, Volt p1,k The phase transition voltage value represents the k-th second data point on characteristic curve segment 1.

[0291] For each first candidate curve segment, the voltage difference parameter between the first candidate curve segment and the characteristic curve segment 1 can be determined using the above formula (2). Based on the voltage difference parameter between the first candidate curve segment and the characteristic curve segment 1, the degree of matching between the curve features of the first candidate curve segment and the curve features of the characteristic curve segment 1 can be determined. For example, if the first candidate curve segment includes candidate curve segment 1, candidate curve segment 2 and candidate curve segment 3, and if the degree of matching between candidate curve segment 1 and characteristic curve segment 1 is the largest, then candidate curve segment 1 can be used as the target curve segment of characteristic curve segment 1. If the difference between the degree of matching 1 between candidate curve segment 1 and characteristic curve segment 1 and the degree of matching 2 between candidate curve segment 2 and characteristic curve segment 1 is less than a preset value, it means that the degree of matching 1 and the degree of matching 2 are approximately the same. Even though the degree of matching between candidate curve segment 1 and characteristic curve segment 1 is the largest, either candidate curve segment 1 or candidate curve segment 2 can be used as the target candidate curve segment of characteristic curve segment 1.

[0292] For the aforementioned feature curve segment 2, which has n data points, the degree of matching between the curve features of each first candidate curve segment and the curve features of feature curve segment 2 can be determined by the following formula (3). It should be noted that, in this case, each first candidate curve segment refers to multiple first candidate curve segments obtained by dividing the first feature curve along the horizontal axis according to the rectangular window corresponding to feature curve segment 2.

[0293]

[0294] Among them, Volt p2,k The phase transition voltage value represents the k-th second data point on characteristic curve segment 2.

[0295] For each first candidate curve segment, the voltage difference parameter between the first candidate curve segment and the characteristic curve segment 2 can be determined using the above formula (3). Based on the voltage difference parameter between the first candidate curve segment and the characteristic curve segment 1, the matching degree between the curve features of the first candidate curve segment and the curve features of the characteristic curve segment 2 is determined. If the matching degree between the first candidate curve segment 2 and the characteristic curve segment 2 is the largest, then the first candidate curve segment 2 is taken as the target curve segment of the characteristic curve segment 2.

[0296] The method for determining the target curve segment of characteristic curve segment 3 is the same as the method for determining the target curve segments of characteristic curve segment 1 and characteristic curve segment 2 described above, and will not be repeated here.

[0297] The method provided in this embodiment determines the degree of matching between the curve features of the first candidate curve segment and the curve features of the preset characteristic curve segment based on the characteristic voltage value of the first data point on the first candidate curve segment and the phase transition voltage value of the second data point on the preset characteristic curve segment. Based on the matching degree corresponding to each first candidate curve segment, a target candidate curve segment that matches the preset characteristic curve segment is determined from multiple first candidate curve segments. This makes the phase transition point determined based on the target curve segment more accurate, improves the accuracy of the phase transition feature data corresponding to the determined phase transition point, and thus improves the accuracy of the maximum capacity determined based on the phase transition feature data and the accuracy of the health status estimation.

[0298] In one embodiment, referring to Figure 12, which is a flowchart illustrating a matching degree determination method provided by an embodiment of this application, this embodiment relates to a possible implementation of determining the matching degree between the curve features of a first candidate curve segment and the curve features of a preset characteristic curve segment based on the characteristic voltage value of a first data point on a candidate curve segment and the phase transition voltage value of a second data point on a preset characteristic curve segment. Based on the above embodiment, the method may include the following steps S1201-S1203.

[0299] S1201, the characteristic voltage value of the first data point in the first candidate curve segment is converted to obtain the target voltage value.

[0300] The number of first data points in the first candidate curve segment can be multiple. The characteristic voltage values ​​of multiple first data points are transformed to obtain the target voltage value corresponding to each first data point.

[0301] For example, the target voltage value can be obtained by converting the characteristic voltage value using the following formula (4):

[0302] Volt t,k =p1*Volt interp,k +p0 (4)

[0303] Among them, Volt t,k Let k represent the target voltage of the first data point, where k is greater than or equal to 1 and not greater than the total number of data points on the characteristic curve segment, and Volt. interp,k The characteristic voltage value represents the k-th first data point on the first candidate curve segment.

[0304] The characteristic voltage values ​​of each first data point can be converted according to other variations or equivalent formulas of the above formula (4) to obtain the target voltage values ​​of each first data point.

[0305] S1202, calculate the voltage difference between the target voltage value of the first data point and the phase transition voltage value of the corresponding second data point.

[0306] Voltage difference = Volt p,k -Volt t,k Or, voltage difference = Volt t,k -Volt p,k .

[0307] S1203, determine the degree of matching between the curve characteristics of the first candidate curve segment and the curve characteristics of the preset characteristic curve segment based on the voltage difference.

[0308] As described in formula (2) above, the voltage difference between the target voltage value of m first data points and the phase transition voltage value of the corresponding second data points can be calculated. The average of the sum of the squares of the m voltage differences is used as a voltage difference parameter between a first candidate curve segment and characteristic curve segment 1. Based on this voltage difference parameter, the degree of matching between the first candidate curve segment and characteristic curve segment 1 can be determined. Alternatively, the square root of this average value can be used as the voltage difference parameter between the first candidate curve segment and characteristic curve segment 1. Based on this voltage difference parameter, the degree of matching between the first candidate curve segment and characteristic curve segment 1 can be determined.

[0309] The method provided in this embodiment converts the characteristic voltage value of the first data point in the first candidate curve segment to obtain the target voltage value, calculates the voltage difference between the target voltage value of the first data point and the phase transition voltage value of the corresponding second data point, and determines the degree of matching between the curve features of the first candidate curve segment and the curve features of the characteristic curve segment based on the voltage difference. This facilitates the determination of the target candidate curve segment based on the degree of matching corresponding to each first candidate curve segment, thereby improving the accuracy of determining the phase transition point and the accuracy of the phase transition feature data corresponding to the phase transition point.

[0310] In one embodiment, the process in S1102 described above, which determines the target candidate curve segment that matches the preset feature curve segment from multiple first candidate curve segments based on the matching degree corresponding to each first candidate curve segment, can be implemented in the following way:

[0311] The first candidate curve segment corresponding to the highest matching degree is taken as the target candidate curve segment that matches the preset feature curve segment.

[0312] The method provided in this embodiment improves the accuracy of the obtained target candidate curve segments by using the first candidate curve segment corresponding to the maximum matching degree as the target candidate curve segment that matches the feature curve segment, thereby improving the accuracy of the phase transition feature data corresponding to the obtained phase transition point.

[0313] In one embodiment, referring to Figure 13, which is a third schematic flowchart of the phase transition characteristic data determination method provided in this application embodiment, this embodiment relates to a possible implementation of how to determine the phase transition characteristic data corresponding to each phase transition point of the battery cell based on parameter correspondence. Based on the above embodiment, when the parameter correspondence includes a first characteristic curve, the method may include the following steps S1301-S1303.

[0314] S1301, obtain the parameter correspondence of the reference cell.

[0315] The correspondence between these parameters can be presented, for example, in the form of a curve, which is called the second characteristic curve for easy distinction and explanation.

[0316] In this step, a second characteristic curve of a reference cell can be obtained. The reference cell can be any cell in the battery pack. For example, the battery pack includes 100 cells, and the cell in S101 is cell number 1 in the battery pack. The reference cell can be any one of cells numbered 2 to 100, or it can be determined based on the voltage values ​​of cells numbered 2 to 100, for example, using the cell with the lowest voltage value as the reference cell. The method for obtaining the second characteristic curve is the same as the method for obtaining the first characteristic curve, that is, the second characteristic curve can be determined based on the characteristic voltage sequence corresponding to the reference cell.

[0317] For example, taking the first characteristic curve as a curve representing the correspondence between the characteristic voltage value and the charging capacity, where the first characteristic curve is a curve with the charging capacity on the horizontal axis and the characteristic voltage value on the vertical axis, then the second characteristic curve is a curve representing the correspondence between the characteristic voltage value and the charging capacity of the reference cell, where the charging capacity on the horizontal axis and the characteristic voltage value on the vertical axis, that is, the second characteristic curve corresponds to the first characteristic curve.

[0318] S1302, based on the length of the second feature curve in the horizontal direction and the second preset number of parts, the first feature curve is divided into multiple second candidate curve segments along the horizontal direction.

[0319] Specifically, the quotient of the length of the second feature curve in the horizontal direction and the second preset number of segments can be determined. This quotient is used as the translation distance of the second feature curve in the horizontal direction. Based on this translation distance, the first feature curve is divided into multiple second candidate curve segments along the horizontal direction. The number of second candidate curve segments is equal to the second preset number of segments.

[0320] S1303, Match the second characteristic curve with multiple second candidate curve segments to determine the phase transition characteristic data corresponding to each phase transition point.

[0321] The second characteristic curve is matched with multiple second candidate curve segments to determine the degree of matching for each segment. The second candidate curve segment with the highest degree of matching is selected as the target candidate curve segment. The midpoint, start point, end point, or any other point on the target candidate curve segment can be considered a phase transition point, thereby determining the phase transition characteristic data corresponding to that point. It should be noted that only one target candidate curve segment can be determined based on one second characteristic curve; therefore, multiple target candidate curve segments need to be determined based on multiple second characteristic curves to identify multiple phase transition points and their corresponding phase transition characteristic data.

[0322] In one possible implementation, the degree of difference between the second feature curve and each second candidate curve segment can be determined, and the second candidate curve segment corresponding to the smallest degree of difference can be taken as the target candidate curve segment.

[0323] In another possible implementation, the similarity between the second feature curve and each second candidate curve segment can be determined, and the second candidate curve segment with the highest similarity can be taken as the target candidate curve segment.

[0324] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0325] The method provided in this embodiment obtains a second characteristic curve of a reference cell. Based on the length of the second characteristic curve along the horizontal axis and a second preset number of segments, the first characteristic curve is divided into multiple second candidate curve segments along the horizontal axis. The second characteristic curve is then matched with these multiple second candidate curve segments to determine the phase transition characteristic data corresponding to each phase transition point. By dividing the first characteristic curve into finer-grained second candidate curve segments, and by gradually matching each fine-grained second candidate curve segment with the second characteristic curve, the phase transition characteristic data can be determined more accurately and quickly.

[0326] In one embodiment, referring to Figure 14, which is a flowchart of the method for determining phase transition characteristic data provided in this application embodiment, this embodiment relates to a possible implementation of matching a second characteristic curve with multiple second candidate curve segments to determine the phase transition characteristic data corresponding to each phase transition point. Based on the above embodiment, S1303 may include the following steps S1401-S1402.

[0327] S1401, for each second candidate curve segment, make the average value of the ordinates of all points on the second characteristic curve equal to the average value of the ordinates of all points on the second candidate curve segment, and then calculate the difference between the second characteristic curve after the average values ​​are equal and the second candidate curve segment to obtain the error value.

[0328] Referring to Figure 15, which is a schematic diagram of the process of matching a second feature curve with multiple second candidate curve segments according to an embodiment of this application, the second feature curve 22 on the first feature curve 11 is aligned to the left with the first feature curve 11 in the same coordinate system. As shown in Figure 15, the first feature curve is the curve at the position of the first feature curve 11 in Figure 15, and the second feature curve is the curve at the position of the second feature curve 22 in Figure 15.

[0329] The curve corresponding to the projection area of ​​the first characteristic curve 11 and the second characteristic curve 22 on the horizontal axis is taken as the first second candidate curve segment. For ease of explanation, the first second candidate curve segment is defined as curve 11_0. The average value of the ordinates of all points on the second characteristic curve 22 located at the position in Figure 15 is set to the average value of curve 11_0, and curve 22 is moved downward by a certain distance to obtain curve 22_0 as shown in Figure 15. Then, the voltage corresponding to each point of curve 11_0 and curve 22_0 is subtracted to obtain the corresponding error value. The mean squared error (MSE) is calculated based on the error value corresponding to each point. The mean squared error is used as the second characteristic curve after the average value is calculated, and the difference is calculated with the second candidate curve segment to obtain the error value. For ease of explanation later, this error value is referred to as the error value corresponding to curve group 0, which includes curve 11_0 and curve 22_0. Alternatively, the average value of the error values ​​corresponding to each point is taken as the error value corresponding to curve group 0.

[0330] By shifting the second characteristic curve 22, located in Figure 15, to the right by the shift distance mentioned in S1302 above, and then, similar to the process of determining the error value between curve 22_0 and curve 11_0, the shifted second characteristic curve 22 is moved downwards by a certain distance to obtain curve 22_1. Then, the voltage corresponding to each point of curve 11_1 is subtracted from that of curve 22_1 to obtain the corresponding error value. Based on the error value corresponding to each point, the mean squared error (MSE) is calculated. The second characteristic curve obtained by subtracting the mean squared error from the second candidate curve segment is then used to obtain the error value. That is, the error value is obtained by subtracting curve 22_1 from the second candidate curve segment. This process is repeated to obtain the error value corresponding to curve 22_2 and the third candidate curve segment, which is then subtracted to obtain the error value corresponding to curve group 1. Curve group 1 includes curve 11_1 and curve 22_1, and so on, to obtain the error values ​​corresponding to each curve group, which will not be elaborated here. Curve 22_2 is obtained by shifting the second characteristic curve 22, which is located in Figure 15, to the right by a certain distance, and then shifting the second characteristic curve 22 downward by a certain distance.

[0331] Following the method described above, the second feature curve can be matched with multiple second candidate curve segments to obtain multiple error values. For example, if the number of second candidate curve segments is 200, then 200 sets of error values ​​corresponding to the curves can be obtained.

[0332] S1402, Match the error values ​​corresponding to each second candidate curve segment to determine the phase transition characteristic data corresponding to each phase transition point.

[0333] In one possible implementation, the second candidate curve segment corresponding to the smallest error value can be used as the target candidate curve segment.

[0334] In another possible implementation, if the difference in error values ​​corresponding to two second candidate curve segments is less than a preset threshold, then one of the two second candidate curve segments can be selected as the target candidate curve segment.

[0335] Once a target candidate curve segment is determined, the starting point, midpoint, end point, or any point on the target candidate curve segment can be used as a phase transition point, thereby determining the phase transition characteristic data corresponding to the phase transition point.

[0336] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0337] The method provided in this embodiment involves, for each second candidate curve segment, averaging the ordinates of all points on the second characteristic curve to the average ordinates of all points on the second candidate curve segment. The difference between the averaged second characteristic curve and the second candidate curve segment is then calculated to obtain an error value. Matching is performed based on the error values ​​corresponding to each second candidate curve segment to determine the phase transition feature data corresponding to each phase transition point. Matching with each second candidate curve segment at a fine-grained level improves the matching accuracy, thereby enhancing the accuracy of the obtained phase transition feature data.

[0338] In one embodiment, S1402 described above, which matches the error values ​​corresponding to each second candidate curve segment to determine the phase transition characteristic data corresponding to each phase transition point, can be achieved in the following way:

[0339] Based on the minimum error value, the corresponding second candidate curve segment is matched to determine the phase transition characteristic data corresponding to each phase transition point.

[0340] Matching the second candidate curve segment based on the minimum error value means using the second candidate curve segment corresponding to the minimum error value as the target candidate curve segment for matching.

[0341] The method provided in this embodiment determines the phase transition feature data corresponding to each phase transition point by matching the corresponding second candidate curve segment based on the minimum error value. Since the second candidate curve segment corresponding to the minimum error value is the curve segment most similar to the second feature curve, the phase transition feature data corresponding to the phase transition point determined based on the most similar curve segment is more accurate.

[0342] In one embodiment, when the parameter correspondence includes the first characteristic curve, determining the phase transition characteristic data corresponding to each phase transition point of the battery cell based on the parameter correspondence can be achieved in the following way:

[0343] The first characteristic curve is processed using a preset analysis algorithm to obtain phase transition peak information, and the phase transition characteristic data corresponding to each phase transition point is determined based on the phase transition peak information.

[0344] A preset analysis algorithm, such as ICA or DVA, can be used to process the first characteristic curve to obtain phase transition peak information. Based on the phase transition peak information, the phase transition characteristic data corresponding to each phase transition point can be determined. The phase transition peak information may include, but is not limited to, the charging capacity corresponding to multiple peak data points, and there is a one-to-one correspondence between the peak data points and the phase transition points.

[0345] In this embodiment, phase transition characteristic data corresponding to multiple phase transition points can be determined based on phase transition peak information. This allows a preset analysis algorithm to mine the characteristic changes of data in the first characteristic curve based on the phase transition characteristic data corresponding to multiple phase transition points, thereby assessing the health status. This method is relatively simple and quick to determine the health status, thereby improving the efficiency of health status determination and enhancing the applicability and accuracy of the algorithm.

[0346] The method provided in this embodiment processes the first characteristic curve using a preset analysis algorithm to obtain phase transition peak information, and determines the phase transition characteristic data corresponding to each phase transition point based on the phase transition peak information. This can improve the accuracy of the phase transition characteristic data corresponding to multiple phase transition points, so that a more accurate SOH estimation can be performed based on the phase transition characteristic data corresponding to multiple phase transition points.

[0347] In one embodiment, referring to Figure 16, which is a flowchart illustrating a method for determining the health status of a battery cell according to an embodiment of this application, this embodiment relates to a possible implementation of determining the health status of a battery cell based on phase transition characteristic data corresponding to each phase transition point of the battery cell. Based on the above embodiment, S104 may include the following steps S1601-S1602.

[0348] S1601 determines the maximum capacity of the battery cell based on the phase change characteristic data corresponding to each phase change point of the battery cell.

[0349] In one possible implementation, if there are two phase transition points, the capacity difference between the charging capacity in the phase transition feature data of the two phase transition points and the SOC difference between the SOC in the phase transition feature data of the two phase transition points can be determined. The quotient of the capacity difference divided by the SOC difference is used as the reference maximum capacity of the cell, and then the maximum capacity of the cell is determined based on the reference maximum capacity.

[0350] In another possible implementation, if there are more than two phase transition points, the phase transition characteristic data corresponding to each phase transition point can be linearly fitted to obtain a fitted line. The slope of the fitted line is used as the reference maximum capacity of the cell, and then the maximum capacity of the cell is determined based on the reference maximum capacity.

[0351] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0352] S1602 determines the health status of a battery cell based on its maximum capacity and rated capacity.

[0353] The ratio of the maximum capacity to the rated capacity of the cell can be determined, and this ratio can be used as the health status of the cell.

[0354] The method provided in this embodiment determines the maximum capacity of the battery cell based on the phase change characteristic data corresponding to each phase change point, and determines the health status of the battery cell based on the maximum capacity and the rated capacity of the battery cell. Since the phase change characteristic data can accurately reflect the changes in the health status of the battery cell, the accuracy of the health status of the battery cell obtained based on the phase change characteristic data corresponding to each phase change point is high.

[0355] In one embodiment, referring to Figure 17, which is a flowchart illustrating a method for determining the maximum capacity provided by an embodiment of this application, this embodiment relates to a possible implementation of how to determine the maximum capacity of a battery cell based on the phase transition characteristic data corresponding to each phase transition point of the battery cell. Based on the above embodiment, S1601 may include the following steps S1701-S1703.

[0356] S1701, determine the attenuation type corresponding to each phase transition point based on the phase transition characteristic data corresponding to each phase transition point. The attenuation type includes rapid attenuation type or slow attenuation type.

[0357] In this step, the attenuation type corresponding to each phase transition point can be determined based on the phase transition characteristic data corresponding to each phase transition point. The attenuation type may include, but is not limited to, rapid attenuation or slow attenuation. In the embodiments of this application, the attenuation type corresponding to the phase transition point may be either rapid or slow attenuation.

[0358] In some embodiments, the attenuation type corresponding to each phase transition point can be determined according to a preset cell aging law based on the phase transition characteristic data corresponding to each phase transition point. The preset cell aging law may differ for different types of cells.

[0359] In one possible implementation, the preset cell aging pattern can include a slow decay type corresponding to phase transition points with a state of charge less than a preset state of charge, and a fast decay type corresponding to each phase transition point with a state of charge greater than or equal to the preset state of charge.

[0360] In another possible implementation, the preset cell aging pattern may include, but is not limited to, phase transition points with a charging capacity less than the first preset charging capacity corresponding to a slow decay type, and phase transition points with a charging capacity greater than or equal to the first preset charging capacity corresponding to a rapid decay type.

[0361] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0362] For example, all phase transition points in the embodiments of this application may correspond to the same attenuation type.

[0363] As another example, in the embodiments of this application, the attenuation type corresponding to some phase transition points can be either a rapid attenuation type or a slow attenuation type. The attenuation type corresponding to other phase transition points can be both a rapid and a slow attenuation type. For example, the preset cell aging law may include, but is not limited to, phase transition points with a charging capacity less than a second preset charging capacity corresponding to a slow attenuation type, and phase transition points with a charging capacity greater than a third preset charging capacity corresponding to a rapid attenuation type. The third preset charging capacity may be less than the second preset charging capacity.

[0364] S1702, based on the slope obtained by linear fitting of the phase transition characteristic data corresponding to the phase transition points belonging to the same decay type, the reference maximum capacity is determined.

[0365] In this step, the reference maximum capacity corresponding to the same attenuation type can be obtained based on the SOC and charging capacity corresponding to each phase transition point belonging to the same attenuation type.

[0366] For example, if the same decay type includes a fast decay type, the reference maximum capacity value corresponding to the fast decay type can be obtained based on the SOC and charging capacity corresponding to each phase transition point of the fast decay type.

[0367] For example, if the same decay type includes a slow decay type, the reference maximum capacity value corresponding to the slow decay type can be obtained based on the SOC and charging capacity corresponding to each phase transition point of the slow decay type.

[0368] In some embodiments, the SOC and charging capacity at each phase transition point of the same degradation type can be linearly fitted to obtain a fitted line, and the reference maximum capacity value corresponding to the same degradation type can be determined based on the slope of the fitted line. The slope of the fitted line can be used as the reference maximum capacity value corresponding to the same degradation type. Alternatively, the slope of the fitted line can be corrected to obtain a corrected result, which can then be used as the reference maximum capacity value corresponding to the same degradation type.

[0369] S1703 determines the maximum capacity of the cell based on the reference maximum capacity corresponding to each attenuation type.

[0370] In this step, the maximum capacity of the battery cell can be determined based on the reference maximum capacity value corresponding to each attenuation type.

[0371] For example, the maximum capacity of a battery cell can be determined by linear or nonlinear combination of the reference maximum capacity values ​​corresponding to each attenuation type.

[0372] In one possible implementation, if the attenuation type corresponding to each phase transition point of the cell includes a rapid attenuation type, the maximum capacity value of the cell can be determined based on the reference maximum capacity value corresponding to the rapid attenuation type.

[0373] For example, the maximum capacity of the cell can be determined according to the following formula (5) based on the reference maximum capacity value corresponding to the rapid decay type.

[0374] Q max =k1*Q max_fast +b1 (5)

[0375] Among them, Q max k1 represents the maximum capacity of the battery cell; Q represents the first preset proportional coefficient; max_fast b1 represents the reference maximum capacity value corresponding to the rapid decay type; b1 represents the second preset proportional coefficient.

[0376] Of course, the maximum capacity of the cell can also be determined according to other variations or equivalent formulas of the above formula (5) based on the reference maximum capacity value corresponding to the rapid decay type.

[0377] In another possible implementation, if the attenuation type corresponding to each phase transition point of the cell includes a slow attenuation type, the maximum capacity value of the cell can be determined based on the reference maximum capacity value corresponding to the slow attenuation type.

[0378] For example, the maximum capacity of the cell can be determined according to the following formula (6) based on the reference maximum capacity value corresponding to the slow decay type.

[0379] Q max =k2*Q max_slow+b2 (6)

[0380] Where k2 represents the third preset proportional coefficient; Q max_slow b1 represents the reference maximum capacity value corresponding to the fast / slow reduction type; b2 represents the fourth preset ratio coefficient.

[0381] Of course, the battery cell health status determination device can determine the maximum capacity value of the battery cell according to other variations or equivalent formulas of the above formula (6) based on the reference maximum capacity value corresponding to the slow decay type.

[0382] In another possible implementation, when the attenuation type corresponding to each phase transition point of the battery cell includes both rapid attenuation and slow attenuation types, the battery cell health status determination device can determine the maximum capacity value of the battery cell based on the reference maximum capacity value corresponding to the rapid attenuation type and the reference maximum capacity value corresponding to the slow attenuation type.

[0383] For example, the cell health status determination device can determine the maximum capacity value of the cell according to the following formula (7) based on the reference maximum capacity value corresponding to the rapid decay type and the reference maximum capacity value corresponding to the slow decay type.

[0384] Q max =k3*Q max_fast +k4*Q max_slow +b3 (7)

[0385] Where k3 represents the fifth preset scaling factor; k4 represents the sixth preset scaling factor; and b3 represents the seventh preset scaling factor.

[0386] Of course, the battery cell health status determination device can determine the maximum capacity value of the battery cell according to other variations or equivalent formulas of the above formula (7) based on the reference maximum capacity value corresponding to the rapid decay type and the reference maximum capacity value corresponding to the slow decay type.

[0387] It should be noted that any preset proportional coefficient mentioned above in the embodiments of this application can be a proportional coefficient determined according to the aging law of the corresponding cell system.

[0388] Another example is that the maximum capacity of the cell can be determined according to the following formula (8) based on the reference maximum capacity value corresponding to the rapid decay type and the reference maximum capacity value corresponding to the slow decay type.

[0389] Q max =f(Q) max_fast Q max_slow (8)

[0390] Where f represents a preset nonlinear function.

[0391] Of course, the maximum capacity of the battery cell can be determined according to other variations or equivalent formulas of the above formula (8) based on the reference maximum capacity value corresponding to the rapid decay type and the reference maximum capacity value corresponding to the slow decay type.

[0392] The above methods are merely examples and should not be construed as limiting the solutions provided in the embodiments of this application. In some embodiments, other methods may also be used to achieve the same results.

[0393] The method provided in this embodiment determines the attenuation type corresponding to each phase transition point based on the state of charge corresponding to each phase transition point. Based on the slope obtained by linearly fitting the phase transition characteristic data corresponding to phase transition points belonging to the same attenuation type, a reference maximum capacity is determined. Then, the maximum capacity of the battery cell is determined based on the reference maximum capacity corresponding to each attenuation type. Because this embodiment distinguishes the attenuation type of the phase transition point, determining the reference maximum capacity based on the attenuation type, and then determining the maximum capacity of the battery cell based on the reference maximum capacity, a more accurate maximum capacity of the battery cell can be obtained. This allows for a more accurate determination of the state of charge (SOH) of the battery cell based on the maximum capacity value.

[0394] In some embodiments, S1701 described above, determining the attenuation type corresponding to each phase transition point based on the phase transition characteristic data corresponding to each phase transition point, can be achieved in the following way:

[0395] Based on the phase transition characteristic data corresponding to each phase transition point, the attenuation type corresponding to each phase transition point is determined according to the preset cell aging law.

[0396] The method provided in this embodiment determines the attenuation type corresponding to each phase transition point based on the phase transition characteristic data corresponding to each phase transition point and in accordance with the preset cell aging law, so that the determined attenuation type conforms to the cell aging law, thereby improving the practicality and effectiveness of the obtained attenuation type.

[0397] In some embodiments, FIG18 is a flowchart illustrating another method for determining the health status of a battery cell provided in this application. Based on any of the above embodiments, this application further describes the overall flow of the method for determining the health status of a battery cell. As shown in FIG18, the method of this application embodiment may include the following steps S1801-S1810:

[0398] S1801, obtain the current value sequence and voltage value sequence of the battery cell during variable current charging.

[0399] S1802 determines multiple charging periods based on the current value sequence.

[0400] S1803, obtain the current value subsequence and voltage value subsequence corresponding to each charging period.

[0401] S1804: Input the current value subsequence and voltage value subsequence of any two adjacent charging periods into the battery model to calibrate the parameters of the battery model and obtain the target battery model after parameter calibration.

[0402] S1805, based on the total charging capacity corresponding to two adjacent charging periods, determine the characteristic current subsequence of the cell under constant simulated current, and the characteristic current subsequence corresponds to the two adjacent charging periods.

[0403] S1806, input the characteristic current subsequence into the target battery model to obtain the characteristic voltage subsequence of the cell under constant simulated current.

[0404] S1807 determines the characteristic voltage sequence based on all characteristic voltage sub-sequences of the cell under constant analog current.

[0405] S1808 determines the parameter correspondence of the battery cell based on the characteristic voltage sequence, and determines the phase transition characteristic data corresponding to each phase transition point of the battery cell based on the parameter correspondence.

[0406] S1809 determines the maximum capacity of the battery cell based on the phase change characteristic data corresponding to each phase change point of the battery cell.

[0407] S1810 determines the health status of a cell based on its maximum capacity and the cell's rated capacity.

[0408] The method provided in this embodiment, because a characteristic voltage subsequence corresponds to only two adjacent charging periods, can accurately capture the current and voltage value subsequences before or after the current change moment in any two adjacent charging periods. Moreover, the current change time between two adjacent charging periods is short, and the internal mechanism changes of the cell are relatively small. Furthermore, the battery model is calibrated online based on the current and voltage value subsequences of two adjacent charging periods. The characteristic voltage subsequence is determined using the online calibrated target battery model and the characteristic current subsequence obtained based on the current value subsequences of two adjacent charging periods. This makes the characteristic voltage subsequence more consistent with the actual charging scenario of the cell, resulting in an accurate and relatively smooth transition characteristic voltage subsequence. Consequently, the characteristic voltage sequence obtained based on the accurate and relatively smooth transition characteristic voltage subsequence is more accurate, and the phase change characteristic data obtained based on the characteristic voltage sequence is more accurate, ultimately leading to a more accurate health status of the cell.

[0409] It should be noted that the specific implementation methods of each step in the embodiments of this application can be referred to the relevant content in any of the above embodiments, and will not be repeated here.

[0410] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0411] Based on the same inventive concept, this application also provides a battery cell health status determination device for implementing the above-described battery cell health status determination method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the battery cell health status determination device provided below can be found in the limitations of the battery cell health status determination method described above, and will not be repeated here.

[0412] In some embodiments, FIG19 is a schematic diagram of a battery cell health status determination device provided in an embodiment of the present application. As shown in FIG19, the device 1900 may include: an acquisition module 1901, a first determination module 1902, a second determination module 1903 and a third determination module 1904.

[0413] The acquisition module 1901 is configured to acquire the current value sequence and voltage value sequence of the battery cell during variable current charging.

[0414] The first determining module 1902 is configured to determine the characteristic voltage sequence of the battery cell under a constant analog current based on the current value sequence and voltage value sequence of the battery cell; the constant analog current is a preset charging current;

[0415] The second determining module 1903 is configured to determine the parameter correspondence of the battery cell based on the characteristic voltage sequence, and to determine the phase change characteristic data corresponding to each phase change point of the battery cell based on the parameter correspondence.

[0416] The third determining module 1904 is configured to determine the health status of the battery cell based on the phase change characteristic data corresponding to each phase change point of the battery cell.

[0417] In some embodiments, the second determining module 1902 includes:

[0418] The first determining submodule is configured to determine multiple charging periods based on a current value sequence;

[0419] The first acquisition submodule is configured to acquire the current value subsequence and voltage value subsequence corresponding to each charging period.

[0420] The second determining submodule is configured to determine the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of at least two charging periods.

[0421] The third determination submodule is configured to determine the characteristic voltage sequence based on all characteristic voltage subsequences of the cell under constant analog current.

[0422] In some embodiments, the first determining module 1902 is configured to obtain a feature voltage sequence based on a current value sequence, a voltage value sequence, a preset charging current, and a pre-trained model.

[0423] In some embodiments, the first determining module 1902 is specifically configured to input the current value sequence, the voltage value sequence, and the preset charging current into the pre-trained model to obtain multiple feature voltage sub-sequences under the preset charging current; and to obtain a feature voltage sequence based on each feature voltage sub-sequence.

[0424] In some embodiments, the second determining submodule is specifically configured to determine the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of any adjacent multiple charging periods.

[0425] In some embodiments, the second determining submodule is specifically configured to determine the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of any two adjacent charging periods in a plurality of charging periods.

[0426] In some embodiments, the first determining submodule is specifically configured to, when the battery cell is in a multi-stage stepped current charging state, acquire each current change moment in the current value sequence; and take a preset time period before or after each current change moment as a charging time period.

[0427] In some embodiments, when the battery cell is in a working state of charging with arbitrary variable current, the current value at each time point in the current value sequence is obtained; if the difference between the current value at a time point and the current value at the previous time point is less than or equal to a first preset threshold, then the time point is taken as a valid time point; and consecutive valid time points are taken as a charging period.

[0428] In some embodiments, the second determining submodule includes:

[0429] The first determining unit is configured to determine the total charging capacity corresponding to at least two charging periods based on a subsequence of current values ​​for at least two charging periods among a plurality of charging periods.

[0430] The second determining unit is configured to determine the characteristic current subsequence of the cell under constant analog current based on the total charging capacity corresponding to at least two charging periods.

[0431] The third determining unit is configured to determine the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of at least two charging periods, and the characteristic current subsequence of the battery cell under constant analog current.

[0432] The third determining unit is specifically configured to input the current value subsequence and voltage value subsequence of at least two charging periods into the battery model to calibrate the parameters of the battery model and obtain the parameter-calibrated target battery model; and to input the characteristic current subsequence of the cell under constant simulated current into the target battery model to obtain the characteristic voltage subsequence of the cell under constant simulated current.

[0433] In some embodiments, the second determining module 1903 includes:

[0434] The second acquisition submodule is configured to acquire preset feature curve segments;

[0435] The matching submodule is configured to match a preset feature curve segment with a first feature curve to determine the phase transition feature data corresponding to each phase transition point.

[0436] In some embodiments, the parameter correspondence includes a first feature curve and a matching submodule, including:

[0437] The fourth determining unit is configured to determine the corresponding rectangular window based on each preset feature curve segment;

[0438] The division unit is configured to divide the first feature curve into multiple first candidate curve segments along the horizontal axis based on each rectangular window and a first preset number of segments.

[0439] The fifth determining unit is configured to determine a target candidate curve segment that matches a preset feature curve segment from a plurality of first candidate curve segments;

[0440] The sixth determining unit is configured to determine the phase transition characteristic data corresponding to each phase transition point based on each target candidate curve segment.

[0441] In some embodiments, the fifth determining unit includes:

[0442] The first determining subunit is configured to, for each first candidate curve segment, determine the degree of matching between the curve features of the first candidate curve segment and the curve features of the preset characteristic curve segment based on the characteristic voltage value of the first data point on the first candidate curve segment and the phase transition voltage value of the second data point on the preset characteristic curve segment; the first data point corresponds to the second data point.

[0443] The second determining subunit is configured to determine, based on the matching degree corresponding to each first candidate curve segment, a target candidate curve segment that matches a preset feature curve segment from a plurality of first candidate curve segments.

[0444] In some embodiments, the first determining subunit is specifically configured to perform a conversion process on the characteristic voltage value of the first data point in the first candidate curve segment to obtain a target voltage value; calculate the voltage difference between the target voltage value of the first data point and the phase transition voltage value of the corresponding second data point; and determine the degree of matching between the curve features of the first candidate curve segment and the curve features of a preset characteristic curve segment based on the voltage difference.

[0445] In some embodiments, the second determining subunit is specifically configured to use the first candidate curve segment corresponding to the maximum matching degree as the target candidate curve segment that matches the preset feature curve segment.

[0446] In some embodiments, the parameter correspondence includes a first characteristic curve, and the second determining module 1903 includes:

[0447] The third acquisition submodule is configured to acquire the second characteristic curve of the reference cell;

[0448] The segmentation module is configured to divide the first feature curve into multiple second candidate curve segments along the horizontal axis based on the length of the second feature curve in the horizontal axis direction and the second preset number of segments.

[0449] The matching submodule is configured to match the second feature curve with multiple second candidate curve segments to determine the phase transition feature data corresponding to each phase transition point.

[0450] In some embodiments, the matching submodule includes:

[0451] The processing unit is configured to, for each second candidate curve segment, make the average value of the ordinates of all points on the second feature curve equal to the average value of the ordinates of all points on the second candidate curve segment, and then perform a difference operation between the second feature curve after the average values ​​are equal and the second candidate curve segment to obtain an error value.

[0452] The seventh determining unit is configured to match the error values ​​corresponding to each second candidate curve segment to determine the phase transition characteristic data corresponding to each phase transition point.

[0453] In some embodiments, the seventh determining unit is specifically configured to determine the phase transition characteristic data corresponding to each phase transition point by matching the corresponding second candidate curve segment based on the minimum error value.

[0454] In some embodiments, the parameter correspondence includes a first characteristic curve and a second determining module 1903, which is specifically configured to process the first characteristic curve using a preset analysis algorithm to obtain phase transition peak information, and determine the phase transition characteristic data corresponding to each phase transition point based on the phase transition peak information.

[0455] In some embodiments, the third determining module 1904 includes:

[0456] The fourth determination submodule is configured to determine the maximum capacity of the battery cell based on the phase change characteristic data corresponding to each phase change point of the battery cell.

[0457] The fifth determination submodule is configured to determine the health status of the battery cell based on the maximum capacity and the rated capacity of the cell.

[0458] In some embodiments, the fourth determining submodule includes:

[0459] The eighth determining unit is configured to determine the attenuation type corresponding to each phase transition point based on the phase transition characteristic data corresponding to each phase transition point. The attenuation type includes a fast attenuation type or a slow attenuation type.

[0460] The ninth determining unit is configured to determine the reference maximum capacity based on the slope obtained by linear fitting of the phase transition characteristic data corresponding to the phase transition points belonging to the same decay type.

[0461] The tenth determining unit is configured to determine the maximum capacity of the cell based on the reference maximum capacity corresponding to each attenuation type.

[0462] In some embodiments, the eighth determining unit is specifically configured to determine the attenuation type corresponding to each phase transition point according to the phase transition characteristic data corresponding to each phase transition point and in accordance with a preset cell aging law.

[0463] The cell health status determination device provided in this application embodiment can be used to execute the technical solutions in the above-described cell health status determination method embodiments of this application. Its implementation principle and technical effects are similar, and will not be repeated here.

[0464] Each module in the aforementioned cell health status determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the cell health status determination device in hardware form or independent of it, or they can be stored in the memory of the cell health status determination device in software form, so that the processor can call and execute the operations corresponding to each module.

[0465] In some embodiments, FIG20 is a schematic diagram of a battery cell health status determination device provided in an embodiment of this application. As shown in FIG20, the battery cell health status determination device provided in this embodiment may include a processor, a memory, and a communication interface connected via a system bus. The processor of the battery cell health status determination device provides computing and control capabilities. The memory of the battery cell health status determination device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the battery cell health status determination device is used for wired or wireless communication with external devices. When the computer program is executed by the processor, it implements the technical solutions in the above-described embodiments of the battery cell health status determination method of this application. The implementation principle and technical effects are similar and will not be repeated here.

[0466] Those skilled in the art will understand that the structure shown in Figure 20 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the cell health status determination device applied thereto. The specific cell health status determination device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0467] In some embodiments, a battery cell health status determination device is also provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the technical solutions in the above-described battery cell health status determination method embodiments of this application. The implementation principle and technical effects are similar, and will not be repeated here.

[0468] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the technical solution in the above-described embodiments of the method for determining the health status of the battery cell. The implementation principle and technical effect are similar, and will not be repeated here.

[0469] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the technical solutions in the above-described embodiments of the cell health status determination method of this application. The implementation principle and technical effects are similar and will not be repeated here.

[0470] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited thereto.

[0471] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for determining the health status of a battery cell, comprising: Obtain the current and voltage value sequences of the battery cell during variable current charging; Based on the current value sequence and voltage value sequence of the battery cell, the characteristic voltage sequence of the battery cell under a constant analog current is determined; the constant analog current is a preset charging current. The parameter correspondence of the battery cell is determined based on the characteristic voltage sequence, and the phase transition characteristic data corresponding to each phase transition point of the battery cell is determined based on the parameter correspondence. as well as The health status of the battery cell is determined based on the phase transition characteristic data corresponding to each phase transition point of the battery cell.

2. The method according to claim 1, wherein determining the characteristic voltage sequence of the battery cell under a constant analog current based on the current value sequence and the voltage value sequence of the battery cell comprises: Multiple charging periods are determined based on the current value sequence; Obtain the current value subsequence and voltage value subsequence corresponding to each of the charging periods; Based on the current value subsequence and voltage value subsequence of at least two charging periods, the characteristic voltage subsequence of the battery cell under constant analog current is determined; as well as The characteristic voltage sequence is determined based on all characteristic voltage sub-sequences of the battery cell under constant simulated current.

3. The method according to claim 1, wherein determining the characteristic voltage sequence of the battery cell under a constant analog current based on the current value sequence and the voltage value sequence of the battery cell includes: The characteristic voltage sequence is obtained based on the current value sequence, the voltage value sequence, the preset charging current, and the pre-trained model.

4. The method according to claim 3, wherein obtaining the feature voltage sequence based on the current value sequence, the voltage value sequence, the preset charging current, and the pre-trained model includes: The current value sequence, the voltage value sequence, and the preset charging current are input into the pre-trained model to obtain multiple feature voltage sub-sequences under the preset charging current; as well as The characteristic voltage sequence is obtained based on each of the characteristic voltage sub-sequences.

5. The method according to claim 2, wherein determining the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of at least two charging periods comprises: Based on the current value subsequence and voltage value subsequence of any multiple adjacent charging periods, the characteristic voltage subsequence of the battery cell under constant simulated current is determined.

6. The method according to claim 2, wherein determining the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of at least two charging periods comprises: Based on the current value subsequence and voltage value subsequence of any two adjacent charging periods in multiple charging periods, the characteristic voltage subsequence of the battery cell under constant simulated current is determined.

7. The method of claim 2, wherein determining a plurality of charging periods based on the current value sequence comprises: When the battery cell is in a multi-stage stepped current charging state, the time of each current change in the current value sequence is obtained. as well as A preset period of time before or after each current fluctuation is considered as a charging period.

8. The method of claim 2, wherein determining the plurality of charging periods based on the current value sequence comprises: When the battery cell is in a working state of charging with arbitrary variable current, the current value at each time point in the current value sequence is obtained; If the difference between the current value at a given time point and the current value at the previous time point is less than or equal to a first preset threshold, then that time point is considered a valid time point; and A series of consecutive effective time points are considered as a charging period.

9. The method according to any one of claims 6-8, wherein determining the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of any two adjacent charging periods in a plurality of charging periods comprises: The total charging capacity corresponding to the at least two charging periods is determined based on the current value subsequence of at least two charging periods among multiple charging periods; Based on the total charging capacity corresponding to the at least two charging periods, the characteristic current subsequence of the battery cell under constant analog current is determined; as well as The characteristic voltage subsequence of the battery cell under constant analog current is determined based on the current value subsequence and voltage value subsequence of the at least two charging periods, and the characteristic current subsequence of the battery cell under constant analog current.

10. The method according to claim 9, wherein determining the characteristic voltage subsequence of the battery cell under constant analog current based on the current value subsequence and voltage value subsequence of the at least two charging periods, and the characteristic current subsequence of the battery cell under constant analog current, comprises: The current and voltage subsequences for at least two charging periods are input into the battery model to calibrate the parameters of the battery model, resulting in a calibrated target battery model; and The characteristic current subsequence of the battery cell under constant simulated current is input into the target battery model to obtain the characteristic voltage subsequence of the battery cell under constant simulated current.

11. The method according to any one of claims 1-10, wherein the parameter correspondence includes a first characteristic curve, and the step of determining the phase transition characteristic data corresponding to each phase transition point of the battery cell according to the parameter correspondence includes: Obtain the preset feature curve segment; as well as The preset feature curve segment is matched with the first feature curve to determine the phase transition feature data corresponding to each phase transition point.

12. The method according to claim 11, wherein matching the preset feature curve segment with the first feature curve to determine the phase transition feature data corresponding to each phase transition point includes: Determine the corresponding rectangular window based on each preset feature curve segment; Based on each of the rectangular windows and the first preset number of parts, the first feature curve is divided into multiple first candidate curve segments along the horizontal axis. Determine a target candidate curve segment from a plurality of first candidate curve segments that matches the preset feature curve segment; and The phase transition feature data corresponding to each phase transition point are determined based on each of the target candidate curve segments.

13. The method of claim 12, wherein determining the target candidate curve segment that matches the preset feature curve segment from a plurality of first candidate curve segments comprises: For each first candidate curve segment, based on the characteristic voltage value of a first data point on the first candidate curve segment and the phase transition voltage value of a second data point on the preset characteristic curve segment, the degree of matching between the curve features of the first candidate curve segment and the curve features of the preset characteristic curve segment is determined; the first data point corresponds to the second data point; and Based on the matching degree of each first candidate curve segment, a target candidate curve segment that matches the preset feature curve segment is determined from a plurality of first candidate curve segments.

14. The method according to claim 13, wherein determining the degree of matching between the curve features of the first candidate curve segment and the curve features of the preset characteristic curve segment based on the characteristic voltage value of a first data point on the first candidate curve segment and the phase transition voltage value of a second data point on the preset characteristic curve segment includes: The characteristic voltage value of the first data point in the first candidate curve segment is transformed to obtain the target voltage value. Calculate the voltage difference between the target voltage value of the first data point and the phase transition voltage value of the corresponding second data point; as well as The degree of matching between the curve features of the first candidate curve segment and the curve features of the preset feature curve segment is determined based on the voltage difference.

15. The method according to claim 13 or 14, wherein determining the target candidate curve segment that matches the preset feature curve segment from a plurality of first candidate curve segments based on the matching degree corresponding to each of the first candidate curve segments includes: The first candidate curve segment corresponding to the highest matching degree is taken as the target candidate curve segment that matches the preset feature curve segment.

16. The method according to any one of claims 1-10, wherein the parameter correspondence includes a first characteristic curve, and the step of determining the phase transition characteristic data corresponding to each phase transition point of the battery cell according to the parameter correspondence includes: Obtain the second characteristic curve of the reference cell; Based on the length of the second feature curve along the horizontal axis and the second preset number of segments, the first feature curve is divided into multiple second candidate curve segments along the horizontal axis; and The second characteristic curve is matched with the plurality of second candidate curve segments to determine the phase transition characteristic data corresponding to each phase transition point.

17. The method of claim 16, wherein matching the second feature curve with the plurality of second candidate curve segments to determine phase transition feature data corresponding to each phase transition point comprises: For each second candidate curve segment, the average value of the ordinates of all points on the second feature curve is made to be equivalent to the average value of the ordinates of all points on the second candidate curve segment. The difference between the second feature curve with the equivalent average value and the second candidate curve segment is calculated to obtain the error value. as well as The phase transition characteristic data corresponding to each phase transition point are determined by matching the error values ​​corresponding to each of the second candidate curve segments.

18. The method according to claim 17, wherein matching based on the error values ​​corresponding to each of the second candidate curve segments to determine the phase transition feature data corresponding to each phase transition point includes: Based on the minimum error value, the corresponding second candidate curve segment is matched to determine the phase transition characteristic data corresponding to each phase transition point.

19. The method according to any one of claims 1-10, wherein the parameter correspondence includes a first characteristic curve, and the step of determining the phase transition characteristic data corresponding to each phase transition point of the battery cell according to the parameter correspondence includes: The first feature curve is processed using a preset analysis algorithm to obtain phase transition peak information, and the phase transition feature data corresponding to each phase transition point is determined based on the phase transition peak information.

20. The method according to any one of claims 1-19, wherein determining the health status of the battery cell based on phase transition characteristic data corresponding to each phase transition point of the battery cell includes: The maximum capacity of the battery cell is determined based on the phase transition characteristic data corresponding to each phase transition point of the battery cell; as well as The health status of the battery cell is determined based on the maximum capacity and the rated capacity of the battery cell.

21. The method according to claim 20, wherein determining the maximum capacity of the battery cell based on phase transition characteristic data corresponding to each phase transition point of the battery cell comprises: The attenuation type corresponding to each phase transition point is determined based on the phase transition characteristic data corresponding to each phase transition point, and the attenuation type includes a fast attenuation type or a slow attenuation type. The reference maximum capacity is determined based on the slope obtained by linear fitting of the phase transition characteristic data corresponding to phase transition points belonging to the same decay type. as well as The maximum capacity of the battery cell is determined based on the reference maximum capacity corresponding to each of the aforementioned attenuation types.

22. The method according to claim 21, wherein determining the attenuation type corresponding to each phase transition point based on the phase transition characteristic data corresponding to each phase transition point includes: Based on the phase transition characteristic data corresponding to each phase transition point, the attenuation type corresponding to each phase transition point is determined according to the preset cell aging law.

23. A device for determining the health status of a battery cell, comprising: The acquisition module is configured to acquire the current value sequence and voltage value sequence of the battery cell during variable current charging. The first determining module is configured to determine the characteristic voltage sequence of the battery cell under a constant analog current based on the current value sequence and the voltage value sequence of the battery cell; the constant analog current is a preset charging current; The second determining module is configured to determine the parameter correspondence of the battery cell based on the characteristic voltage sequence, and to determine the phase change characteristic data corresponding to each phase change point of the battery cell based on the parameter correspondence. The third determining module is configured to determine the health status of the battery cell based on the phase change characteristic data corresponding to each phase change point of the battery cell.

24. A battery cell health status determination device, comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method according to any one of claims 1 to 22.

25. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 22.

26. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 22.

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