Method for estimating state of health of battery and method for establishing capacity estimation model

Abstract

The application discloses a battery state of health estimation method and device, a capacity estimation model establishment method, a vehicle and a computer readable storage medium. The battery state of health estimation method takes the curve similarity between a battery state curve and an initial state curve as a health indicator that is essentially stable in representing the degradation degree of the battery state of health, and performs the calculation of the battery state of health, so that the battery state of health obtained based on the method is more accurate. In addition, the battery state of health estimation method considers that it is difficult to obtain the IC curve of the complete charging and discharging process of the battery in some application scenarios, and the curve similarity is obtained based on the battery state curve and the initial state curve representing the corresponding relationship between the incremental capacity of the battery and the voltage in the at least partial charging or at least partial discharging process of the battery, thereby improving the adaptability and robustness of the battery state of health estimation method.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to battery health state estimation technology in the field of computer technology, and more specifically, to a method for estimating battery health state and a method for establishing a capacity estimation model. Background Technology

[0002] Battery State of Health (SOH) is a parameter indicating the health status of a battery in various application scenarios (such as vehicles, personal electronic devices, network storage, etc.). For many Battery Management Systems (BMS), battery state of health is a key piece of information for the BMS to manage the battery's operating status.

[0003] Therefore, in some usage scenarios, it is necessary to estimate the battery health status, and it is necessary to propose a highly adaptable method for estimating battery health status. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method for estimating battery state of health and a method for establishing a capacity estimation model, thereby improving the adaptability of the battery state of health estimation method.

[0005] To achieve the above technical objectives, the embodiments of this application provide the following technical solutions:

[0006] Firstly, a method for estimating battery health status is provided, including:

[0007] Obtain a battery state curve, which characterizes the relationship between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging.

[0008] Obtain the curve similarity between the battery state curve and the initial state curve, wherein the initial state curve characterizes the correspondence between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging in the initial charge-discharge cycle, and the curve similarity characterizes the similarity difference between the battery state curve and the initial state curve;

[0009] The curve similarity is used as a health indicator to calculate the battery health status.

[0010] In some implementations, using the curve similarity as a health indicator to calculate the battery health status includes:

[0011] Based on the curve similarity and capacity estimation model, the current maximum usable capacity of the battery is obtained. The capacity estimation model represents the calculation relationship between the curve similarity and the current maximum usable capacity of the battery. The capacity estimation model is obtained by fitting historical battery data, which includes the correspondence between historical battery capacity and historical curve similarity.

[0012] The ratio of the battery's current maximum usable capacity to its rated capacity is used as the battery's health status.

[0013] In some implementations, the process of obtaining the curve similarity between the battery state curve and the historical state curve further includes:

[0014] The battery state curve is interpolated and filtered to reduce noise and improve its smoothness.

[0015] In some embodiments, the interpolation and filtering of the battery state curve includes:

[0016] The battery state curve is subjected to cubic spline interpolation and Savitzky-Golay filtering.

[0017] In some embodiments, both the battery state curve and the initial state curve include the correspondence between the incremental capacity and voltage of the battery when it is within a preset SOC range; the preset SOC range is the SOC range that includes the peak segment of the battery incremental capacity and voltage curve.

[0018] In some implementations, obtaining the battery state curve includes:

[0019] Acquire battery condition data, which includes voltage, current, and SOC during battery charge-discharge cycles;

[0020] Extract target data from the battery operating condition data, wherein the target data is discharge segment data or charging segment data of the battery operating condition data;

[0021] When the discharge segment data meets the preset SOC range, the battery state curve is calculated. The preset SOC range is the SOC range that includes the peak segment of the battery incremental capacity and voltage curves.

[0022] In some implementations, the target data for extracting the battery operating condition data includes:

[0023] The current in the battery operating condition data that meets the first preset condition is used as the charging segment data of the battery operating condition data; the first preset condition includes that the current in the battery operating condition data is less than or equal to a preset current value and the SOC has an upward trend.

[0024] or

[0025] The current in the battery operating condition data that meets the second preset condition is taken as the discharge segment data of the battery operating condition data; the second preset condition includes that the current in the battery operating condition data is greater than or equal to a preset current value and the SOC has a decreasing trend.

[0026] In some implementations, obtaining the curve similarity between the battery state curve and the historical state curve includes:

[0027] The similarity between the battery state curve and the historical state curve is calculated using a dynamic time warping algorithm.

[0028] Secondly, a method for establishing a capacity estimation model is provided for estimating the current maximum available capacity of a battery. The method for establishing the capacity estimation model includes:

[0029] Obtain historical battery state curves, which characterize the relationship between incremental battery capacity and voltage during at least partial charging or at least partial discharging of the battery in the past.

[0030] From the historical battery state curves, extract the curve similarity between each historical battery state curve and the initial battery state curve. The curve similarity represents the difference in similarity between the historical battery state curve and the selected historical state curve.

[0031] Using the curve similarity as a health indicator, a capacity estimation model is established, which characterizes the calculation relationship between the curve similarity and the current maximum usable capacity of the battery.

[0032] The capacity estimation model is fitted using historical battery data to determine the model parameters in the capacity estimation model. The historical battery data includes the correspondence between historical battery capacity and historical curve similarity.

[0033] Thirdly, a battery health state estimation device is provided, comprising:

[0034] The curve acquisition module is used to acquire the battery state curve, which represents the relationship between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging.

[0035] The similarity difference module is used to obtain the curve similarity between the battery state curve and the initial state curve. The initial state curve represents the correspondence between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging in the initial charge-discharge cycle. The curve similarity represents the similarity difference between the battery state curve and the initial state curve.

[0036] The health status module is used to calculate the battery health status by using the curve similarity as a health indicator.

[0037] Fourthly, a vehicle is provided, comprising: a controller and a memory; wherein the memory is connected to the controller, the memory is used to store a computer program, and the controller is used to implement the battery health state estimation method as described in any of the preceding claims by running the computer program stored in the memory.

[0038] Fifthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the battery health state estimation method as described in any of the preceding claims or the capacity estimation model establishment method as described above.

[0039] In a sixth aspect, a computer program product or computer program is provided, the computer program product comprising a computer program stored in a computer-readable storage medium; a processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, implements the steps of the above-described method for estimating battery health status or the method for establishing a capacity estimation model as described above.

[0040] As can be seen from the above technical solutions, the embodiments of this application provide a method and apparatus for estimating battery health status, a method for establishing a capacity estimation model, a vehicle, and a computer-readable storage medium. The battery health status estimation method uses the curve similarity between the battery state curve and the initial state curve as a health indicator that is inherently stable in representing the degree of battery health status degradation, and calculates the battery health status accordingly, making the battery health status calculated based on this method more accurate. Furthermore, considering that it is difficult to obtain the IC curve (i.e., the curve representing the correspondence between the incremental capacity and voltage of the battery during the charging and discharging process) of a complete battery charge-discharge process in some application scenarios, the method obtains the curve similarity between the battery state curve (representing the correspondence between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging) and the initial state curve, thereby improving the adaptability and robustness of the battery health status estimation method. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 A schematic diagram illustrating an application scenario provided for one embodiment of this application;

[0043] Figure 2 A schematic diagram illustrating another application scenario provided for one embodiment of this application;

[0044] Figure 3 A flowchart illustrating a method for estimating battery health status according to an embodiment of this application;

[0045] Figure 4 A schematic diagram of a battery state curve provided for one embodiment of this application;

[0046] Figure 5 A comparative schematic diagram of battery state curves with different charge-discharge cycle numbers provided for one embodiment of this application;

[0047] Figure 6 A schematic diagram illustrating the distinction between charging and discharging data is provided as an embodiment of this application;

[0048] Figure 7 A flowchart illustrating a method for establishing a capacity estimation model, provided as an embodiment of this application;

[0049] Figure 8 A schematic diagram of a verification result provided for one embodiment of this application;

[0050] Figure 9 A schematic diagram of a battery health state estimation device provided in one embodiment of this application;

[0051] Figure 10 A schematic diagram of the structure of a car is provided as an embodiment of this application;

[0052] Figure 11 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0053] Unless otherwise defined, the technical or scientific terms used in the embodiments of this specification shall have the ordinary meaning understood by one of ordinary skill in the art to which this specification pertains. The terms "first," "second," and similar terms used in the embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to avoid confusion of constituent elements.

[0054] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this specification. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0055] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0056] Overview

[0057] Currently, some methods for estimating battery state of health (SOH) rely on the relationship between the charged capacity and the initial capacity during charging to obtain a corrected SOH. These methods do not consider the impact of charging rate, temperature, and other factors on the charged capacity, resulting in significant errors in SOH calculation and poor robustness. Other battery health estimation methods require measuring the battery's internal resistance, which is difficult and costly, limiting their applicability.

[0058] To address the aforementioned issues, the inventors, through in-depth research on incremental capacity analysis, proposed a novel health indicator that fundamentally and stably characterizes the degree of battery degradation: the curve similarity between the battery state curve and the initial state curve. Calculating the battery health state based on this health indicator improves the accuracy of the calculation, reduces calculation errors, and enhances the robustness of the battery health state estimation method. Furthermore, considering that in some usage scenarios (such as vehicle power batteries), batteries generally do not undergo a complete charge-discharge cycle (i.e., charging from 0% to 100% or discharging from 100% to 0%), it is difficult to obtain the battery incremental capacity-voltage (IC) curve characterizing a complete charge-discharge cycle. Therefore, in the process of estimating battery health status, the inventors considered using the similarity between the battery state curve, which characterizes the correspondence between incremental capacity and voltage during at least partial charging or at least partial discharging, and the initial state curve as a health indicator. On the one hand, incremental capacity (IC) can be calculated by distinguishing the difference between charging / discharging capacity and the overall voltage change, which has low computational cost and does not require calculation of battery internal resistance, thus reducing estimation costs. On the other hand, compared with estimation methods that require obtaining the IC curve of a complete charge-discharge cycle, it has better adaptability.

[0059] Based on the above ideas, this application provides a method for estimating battery health status. The feasible application scenarios and feasible implementation methods of the battery health status estimation method will be described below with reference to the accompanying drawings.

[0060] Exemplary scenario

[0061] Estimating battery health status is of practical significance in many applications, such as mobile storage (e.g., electric vehicles), stationary storage (e.g., network storage), and portable storage (e.g., personal electronic devices). Battery health status is a crucial parameter for both device management and maintenance personnel. (Reference) Figure 1 and Figure 2 , Figure 1This paper illustrates an application example of estimating battery health status in electric vehicles. In electric vehicles, the battery health status is a key parameter determining the vehicle's range, acceleration, and other parameters. Therefore, the electric vehicle's controller (such as the vehicle controller) can collect various parameters of the battery to estimate its health status. When the battery health status value is low or rapidly declines over a period of time, the system can push graphic and textual prompts indicating "abnormal battery health status" to the user through devices such as the central control screen and instrument panel. This allows the user to take timely measures such as replacing the battery or changing driving habits to mitigate the deterioration of the battery health status.

[0062] Figure 2 This document illustrates an application example of estimating battery health status in personal electronic devices (such as mobile phones and tablets). In this application scenario, the personal electronic device can estimate the battery health status. When the battery health status is below a certain value, it pushes a graphic and text prompt to the user that reads "Battery health status is abnormal, please pay attention to battery health status," so that the user can take timely measures such as replacing the battery, maintenance, and adjusting usage habits to improve the deteriorating battery health status.

[0063] It should be noted that, Figure 1 and Figure 2 The examples provided only illustrate two feasible application scenarios and should not be construed as limiting the application scenarios of the battery health state estimation method provided in this application. The battery health state estimation method provided in this application can also be applied to other application scenarios that require battery health state detection.

[0064] The method for estimating battery health status provided in the embodiments of this application will be described exemplarily below with reference to the accompanying drawings.

[0065] Exemplary methods

[0066] This application provides a method for estimating battery health status, referring to... Figure 3 The method includes:

[0067] S101: Obtain the battery state curve, which represents the relationship between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging.

[0068] refer to Figure 4 , Figure 4 The battery state curves characterizing the relationship between the incremental capacity and voltage during discharge are shown. Figure 4The curve shown can also be called the IC (incremental capacity) curve. In this curve, the horizontal axis is the discharge voltage in volts (V), and the vertical axis is the incremental capacity in ampere-hours (Ah / V). Figure 4 The battery state curve shown can be the IC curve corresponding to the change in the battery's state of charge (SOC) from 80% to 30%. Figure 1 In the application scenario shown, the battery's SOC (State of Charge) data typically varies between 30% and 80%. Therefore, the IC curve corresponding to the battery's SOC varying between 30% and 80% is used as the battery state curve. Of course, Figure 4 The battery state curve shown can also be the IC curve corresponding to the change of battery SOC from 70% to 40%. In other embodiments of this application, the form of the battery state curve may not be an IC curve, as long as the battery state curve can characterize the correspondence between the incremental capacity and voltage of the battery during at least a partial charging process or at least a partial discharging process. This application does not limit this, and it depends on the actual situation.

[0069] S102: Obtain the curve similarity between the battery state curve and the initial state curve, wherein the initial state curve represents the correspondence between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging in the initial charge-discharge cycle, and the curve similarity represents the difference in similarity between the battery state curve and the initial state curve.

[0070] refer to Figure 5 , Figure 5 The incremental capacity curves (IC Curves of Different Cycles) are shown, specifically the battery state curves obtained in the 1st charge-discharge cycle (Cycel1), 201st charge-discharge cycle (Cycel201), 401st charge-discharge cycle (Cycel401), 601st charge-discharge cycle (Cycel601), and 801st charge-discharge cycle (Cycel801). Figure 5 In the graph, the horizontal axis represents voltage in volts (V), and the vertical axis represents incremental capacity in ampere-hours (Ah / V).

[0071] By studying the battery state curves during different battery charging cycles, it was found that the peak height of the battery state curve decreases with the increase of the number of battery charging cycles, and the peak position shifts significantly towards lower voltages. Therefore, the similarity between different battery state curves can effectively characterize the degree of battery degradation. Considering that vehicle batteries often do not undergo a complete charge-discharge process (typically, for reasons such as vehicle power battery protection mechanisms and user charging convenience, vehicle power batteries will not be discharged to 0% before recharging), that is, under normal circumstances, the SOC of a vehicle battery will not discharge from 100% to 0%, this study considers using the similarity between the battery state curve (characterizing the correspondence between the incremental capacity and voltage during at least partial charging or at least partial discharging) and the initial state curve as a health indicator in the process of estimating battery health. On the one hand, the incremental capacity can be calculated by distinguishing the difference between charging / discharging capacity and the overall voltage change, which has low computational cost and does not require calculation of battery internal resistance, thus reducing estimation costs. On the other hand, compared with estimation methods that require obtaining the IC curve of a complete charge-discharge cycle, it has better adaptability.

[0072] S103: Use the curve similarity as a health indicator to calculate the battery health status.

[0073] In step S103, after the health indicator is obtained, it can be substituted into the mapping function between the health indicator and the battery health state obtained by means of linear regression or other methods to calculate the battery health state. In some embodiments, the health indicator can also be used as input to a pre-trained neural network, and the battery health state can be estimated through the prediction of the pre-trained neural network. This application does not limit the specific calculation method.

[0074] In some implementations, a feasible method for calculating the battery health status is provided, specifically, the calculation of the battery health status by using the curve similarity as a health indicator includes:

[0075] S1031: Based on the curve similarity and capacity estimation model, obtain the current maximum usable capacity of the battery. The capacity estimation model represents the calculation relationship between the curve similarity and the current maximum usable capacity of the battery. The capacity estimation model is obtained by fitting historical battery data. The historical battery data includes the correspondence between historical battery capacity and historical curve similarity.

[0076] S1032: The ratio of the current maximum available capacity of the battery to the rated capacity of the battery is taken as the battery health status.

[0077] In this embodiment, a capacity estimation model is obtained by fitting historical battery data. The curve similarity is used as the model input and substituted into the capacity estimation model to calculate the current maximum usable capacity of the battery. This calculation method is characterized by its simple calculation process and low computational resource requirements.

[0078] Referring to Formula (1) below, Formula (1) gives a feasible capacity estimation model.

[0079]

[0080] In formula (1), Q i AD represents the battery's current maximum usable capacity, where A, B, and C are the model parameters for the capacity estimation model. i The curve similarity is given by i (where i is the number of charge-discharge cycles). According to the definition of battery SOH, the ratio of the battery's current maximum usable capacity to its rated capacity is the battery SOH.

[0081] To obtain a battery state curve with high smoothness and low noise, in one embodiment of this application, the method further includes, before obtaining the curve similarity between the battery state curve and the initial state curve:

[0082] The battery state curve is interpolated and filtered to reduce noise and improve its smoothness.

[0083] The purpose of interpolating the battery state curve is to supplement missing data points and improve its smoothness. The purpose of filtering the battery state curve is to reduce noise.

[0084] Because voltage and current data are recorded at equal time intervals, the sampling accuracy at both ends of the battery capacity increment and discharge voltage curve is low, which may lead to large calculation errors in the battery state curve. Therefore, in order to improve the calculation accuracy and smoothness of the battery state curve, interpolation and filtering are performed on the battery state curve in this embodiment.

[0085] Interpolating and filtering the battery state curves can improve the accuracy of subsequent curve similarity acquisition, thereby enhancing the overall accuracy of the battery health state estimation method.

[0086] Optionally, in one embodiment of this application, the interpolation and filtering of the battery state curve includes:

[0087] The battery state curve is subjected to cubic spline interpolation and Savitzky-Golay filtering (SG filtering for short).

[0088] Cubic spline interpolation divides the known data into several segments, constructs a cubic function for each segment, and ensures that the transitions between the piecewise functions have continuity of order 0, continuity of the first derivative, and continuity of the second derivative. This interpolation method is used to interpolate battery state curves, achieving a trade-off between interpolation calculation and preserving all original data.

[0089] The original battery state curve may contain significant noise, making it unsuitable as direct input data for estimating battery health. Filtering is necessary to remove this noise. Compared to other filtering methods, SG filtering tends to remove less high-frequency information while maintaining the basic shape and width of the filtered data. This method is more effective than others when the data changes rapidly. Therefore, considering the characteristics of the battery state curve data, SG filtering is used here to eliminate noise in the battery state curve.

[0090] To improve the accuracy of battery health state estimation, in one embodiment of this application, both the battery state curve and the initial state curve include the correspondence between the incremental capacity and voltage of the battery when it is within a preset SOC range; the preset SOC range is the SOC range that includes the peak segment of the battery incremental capacity and voltage curve.

[0091] Still referencing Figure 5 When the peak values ​​of the battery state-of-the-art (SOC) curves under different charging cycles are distributed between 3.8V and 3.4V, the corresponding SOC range may be 30%–80%. In this case, the preset SOC range can be set to 30%–80% to ensure that the peak segments of both the battery state-of-the-art and initial state-of-the-art curves are included. When the peak segments of the battery's incremental capacity and voltage curves are included within both the battery state-of-the-art and initial state-of-the-art curves, it is easier to calculate the curve similarity between the two curves, thereby improving the accuracy of battery health state estimation.

[0092] Optionally, one embodiment of this application proposes a feasible curve similarity calculation method, specifically, obtaining the curve similarity between the battery state curve and the historical state curve includes:

[0093] The similarity between the battery state curve and the historical state curve is calculated using a dynamic time warping algorithm.

[0094] Dynamic Time Warping (DTW) is an algorithm used to measure the similarity between two sequences (e.g., a battery state curve and a historical state curve), which may differ in voltage or incremental capacity. It measures similarity by comparing two sets of asynchronous signals and finding the optimal matching path between them. In the SOH estimation problem, we primarily focus on measuring the difference in IC amplitude, rather than its distribution along the voltage axis. Therefore, the DTW algorithm is well-suited for measuring the differences in the shape characteristics of two IC curves.

[0095] In one embodiment of this application, a feasible method for obtaining a battery state curve is provided, wherein obtaining the battery state curve includes:

[0096] Acquire battery condition data, which includes voltage, current, and state of charge (SOC) during battery charge-discharge cycles.

[0097] Extract target data from the battery operating condition data, where the target data is either the discharge segment data or the charging segment data of the battery operating condition data. (Reference) Figure 6 , Figure 6 This diagram illustrates the distinction between charging and discharging data; for example, changes in current, SOC, etc., can differentiate between the charging and discharging stages. Figure 6 In the graph, the horizontal axis represents time, and the vertical axis represents SOC.

[0098] When the discharge segment data meets the preset SOC range, the battery state curve is calculated. The preset SOC range is the SOC range that includes the peak segment of the battery incremental capacity and voltage curves.

[0099] Optionally, the target data for extracting the battery operating condition data includes:

[0100] The current in the battery operating condition data that meets the first preset condition is used as the charging segment data of the battery operating condition data; the first preset condition includes that the current in the battery operating condition data is less than or equal to a preset current value and the SOC has an upward trend.

[0101] or

[0102] The current in the battery operating condition data that meets the second preset condition is taken as the discharge segment data of the battery operating condition data; the second preset condition includes that the current in the battery operating condition data is greater than or equal to a preset current value and the SOC has a decreasing trend.

[0103] In this embodiment, by setting a first preset condition and a second preset condition, occasional current fluctuations during battery charging and discharging are excluded, thus avoiding abnormal division of the discharge and charging segments caused by coupled current fluctuations.

[0104] Accordingly, embodiments of this application also provide a method for establishing a capacity estimation model, such as... Figure 7 As shown, the method for establishing the capacity estimation model for estimating the current maximum usable capacity of a battery includes:

[0105] S701: Obtain historical battery state curves, which characterize the relationship between incremental battery capacity and voltage during at least partial charging or at least partial discharging of the battery in the past.

[0106] S702: Extract the curve similarity between each historical battery state curve and the initial battery state curve from the historical battery state curves. The curve similarity represents the difference in similarity between the historical battery state curves and the selected historical state curves.

[0107] S703: Using the curve similarity as a health indicator, a capacity estimation model is established, wherein the capacity estimation model characterizes the calculation relationship between the curve similarity and the current maximum usable capacity of the battery;

[0108] S704: Using historical battery data, fit the capacity estimation model to determine the model parameters in the capacity estimation model. The historical battery data includes the correspondence between historical battery capacity and historical curve similarity. Taking formula (1) as an example, in step S704, determining the model parameters in the capacity estimation model is to determine the three parameters A, B, and C in formula (1).

[0109] The capacity estimation model established in this embodiment belongs to the same concept as the battery health state estimation method provided in the above embodiments of this application. The established capacity estimation model can be applied to the above-mentioned battery health state estimation method. For technical details such as battery state curves and health indicators that are not described in detail in this embodiment, please refer to the relevant content of the battery health state estimation method provided in the above embodiments of this application; they will not be repeated here.

[0110] In one embodiment of this application, an example analysis was performed using publicly available data from the Maryland Institute. A capacity estimation model as shown in formula (1) was established through steps S701 to S703. The three model parameters A, B and C were fitted using historical battery data from the publicly available data from the Maryland Institute, and a capacity estimation model as shown in formula (2) was obtained.

[0111]

[0112] Based on the capacity estimation model shown in formula (2), the battery health state estimation method of the above embodiment is verified by verification data, and the following results are obtained: Figure 8 The verification results shown are as follows: Figure 8 The horizontal axis of both the left and right graphs represents the number of cycles. Figure 8 The vertical axis of the left figure represents battery capacity, measured in ampere-hours (Ah). Different data points represent the measured battery capacity and the estimated battery capacity obtained by the battery health status estimation method provided in this application. Figure 8 The image on the right is Figure 8 The statistical results in the left figure, Figure 8 The vertical axis of the right graph represents the estimated state of health error (SOH Error), in percentages. Figure 8 The data in the right figure represent the root mean square error (RMSE), mean absolute error (MAE), and erroneous data points, respectively.

[0113] pass Figure 8 As shown in the right figure, the root mean square error (RMSE) of the battery health state estimation method provided in this application embodiment is 2.16%, and the mean absolute error (MAE) is 2.98%. This data shows that the battery health state estimation method provided in this application embodiment has high accuracy and robustness.

[0114] Exemplary device

[0115] This application also provides a battery health state estimation device, such as... Figure 9 As shown, it includes:

[0116] The curve acquisition module 100 is used to acquire the battery state curve, which represents the relationship between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging.

[0117] The similarity difference module 200 is used to obtain the curve similarity between the battery state curve and the initial state curve. The initial state curve represents the correspondence between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging in the initial charge-discharge cycle. The curve similarity represents the similarity difference between the battery state curve and the initial state curve.

[0118] The health status module 300 is used to calculate the battery health status by using the curve similarity as a health indicator.

[0119] The battery health state estimation device provided in this embodiment belongs to the same application concept as the battery health state estimation method provided in the above embodiments of this application. It can execute the battery health state estimation method provided in any of the above embodiments of this application and has the corresponding functional modules and beneficial effects for executing the battery health state estimation method. Technical details not described in detail in this embodiment can be found in the specific processing content of the battery health state estimation method provided in the above embodiments of this application, and will not be repeated here.

[0120] Exemplary vehicle

[0121] This application also provides a vehicle, as shown in the embodiments. Figure 10 The system includes a controller 400 and a memory 500; wherein the memory 500 is connected to the controller 400, the memory 500 is used to store a computer program, and the controller 400 is used to implement the battery health state estimation method as described above by running the computer program stored in the memory 500.

[0122] The controller 400 can be a vehicle controller, a battery management system controller, etc., and this application does not limit it to this. The controller 400 and the memory 500 can establish a communication connection through the vehicle's CAN (Controller Area Network) bus.

[0123] Exemplary electronic devices

[0124] Another embodiment of this application also provides an electronic device, see [link to relevant documentation] Figure 11 As shown, an exemplary embodiment of this specification also provides an electronic device, including: a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform steps in the battery health state estimation method or capacity estimation model establishment method according to various embodiments of this specification described above.

[0125] The internal structure of the electronic device can be as follows: Figure 11As shown, the electronic device includes a processor, memory, network interface, and input device connected via a system bus. The processor provides computing and control capabilities. The memory 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 network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it follows the steps in the battery health state estimation method or capacity estimation model establishment method according to various embodiments of this specification described in the above embodiments.

[0126] The processor may include the main processor, as well as baseband chips, modems, etc.

[0127] The memory stores a program that executes the technical solution of this invention, and may also store an operating system and other critical business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0128] The processor can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0129] Input devices may include devices that receive data and information input by the user, such as keyboards, mice, cameras, scanners, light pens, voice input devices, touch screens, pedometers, or gravity sensors.

[0130] Output devices may include devices that allow information to be output to the user, such as displays, printers, speakers, etc.

[0131] The communication interface may include any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0132] The processor executes the program stored in the memory and calls other devices, which can be used to implement the various steps of any of the battery health state estimation methods or capacity estimation model establishment methods provided in the above embodiments of this application.

[0133] The electronic device may also include a display component and a voice component. The display component may be a liquid crystal display screen or an e-ink display screen. The input device of the electronic device may be a touch layer covering the display component, or a button, trackball or touchpad set on the casing of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0134] Those skilled in the art will understand that Figure 11 The structures shown are merely block diagrams of a portion of the structure related to the scheme described in this specification, and do not constitute a limitation on the electronic devices to which the scheme described in this specification is applied. Specific electronic devices may include more or fewer components than those shown in the figures, or may combine certain components, or may have different component arrangements.

[0135] Exemplary computer program products and storage media

[0136] In addition to the methods and devices described above, the battery health state estimation method or capacity estimation model establishment method provided in the embodiments of this specification can also be a computer program product, which includes computer program instructions. When the computer program instructions are run by a processor, the processor causes the processor to perform the steps in the battery health state estimation method or capacity estimation model establishment method according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0137] The computer program product described herein can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments described herein. These programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0138] Furthermore, embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the battery health state estimation method or capacity estimation model establishment method according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0139] 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, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this specification can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0140] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The embodiments described above are merely illustrative of several implementation methods outlined in this specification. While the descriptions are specific and detailed, they should not be construed as limiting the scope of the solutions provided in this specification. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this specification, and these all fall within the scope of protection of this specification. Therefore, the scope of protection for this patent should be determined by the appended claims.

Claims

1. A method for estimating the state of health of a battery, characterized in that, include: Obtain a battery state curve, which characterizes the relationship between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging. Obtain the curve similarity between the battery state curve and the initial state curve, wherein the initial state curve characterizes the correspondence between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging in the initial charge-discharge cycle, and the curve similarity characterizes the similarity difference between the battery state curve and the initial state curve; Using the curve similarity as a health indicator, the battery health status is calculated. Both the battery state curve and the initial state curve include the correspondence between the incremental capacity and voltage of the battery within a preset SOC range. The preset SOC range is the SOC range including the peak segment of the battery's incremental capacity and voltage curves. Calculating the battery health status using the curve similarity as a health indicator includes: obtaining the battery's current maximum usable capacity based on the curve similarity and a capacity estimation model; and using the ratio of the current maximum usable capacity to the battery's rated capacity as the battery health status. The capacity estimation model is determined using the following formula: Where Qi is the current maximum available capacity of the battery, A, B, and C are the model parameters of the capacity estimation model, ADi is the curve similarity, and i is the number of charge-discharge cycles.

2. The method according to claim 1, characterized in that, The capacity estimation model characterizes the calculation relationship between the curve similarity and the current maximum usable capacity of the battery. The capacity estimation model is obtained by fitting historical battery data, which includes the correspondence between historical battery capacity and historical curve similarity.

3. The method according to claim 1, characterized in that, Before obtaining the curve similarity between the battery state curve and the initial state curve, the following steps are also included: The battery state curve is interpolated and filtered to reduce noise and improve its smoothness.

4. The method according to claim 3, characterized in that, The interpolation and filtering of the battery state curve includes: The battery state curve is subjected to cubic spline interpolation and Savitzky-Golay filtering.

5. The method according to any one of claims 1 to 4, characterized in that, The battery state curve acquisition includes: Acquire battery condition data, which includes voltage, current, and state of charge (SOC) during battery charge-discharge cycles; Extract target data from the battery operating condition data, wherein the target data is discharge segment data or charging segment data of the battery operating condition data; When the discharge segment data meets the preset SOC range, the battery state curve is calculated. The preset SOC range is the SOC range that includes the peak segment of the battery incremental capacity and voltage curves.

6. The method according to claim 5, characterized in that, The target data for extracting the battery operating condition data includes: The current in the battery operating condition data that meets the first preset condition is used as the charging segment data of the battery operating condition data; the first preset condition includes that the current in the battery operating condition data is less than or equal to a preset current value and the SOC has an upward trend. or The current in the battery operating condition data that meets the second preset condition is taken as the discharge segment data of the battery operating condition data; the second preset condition includes that the current in the battery operating condition data is greater than or equal to a preset current value and the SOC has a decreasing trend.

7. The method according to any one of claims 1 to 4, characterized in that, The process of obtaining the curve similarity between the battery state curve and the initial state curve includes: The similarity between the battery state curve and the initial state curve is calculated using a dynamic time warping algorithm.

8. A method for establishing a capacity estimation model, characterized in that, The method for establishing the capacity estimation model used to estimate the current maximum usable capacity of a battery includes: Obtain historical battery state curves, which characterize the relationship between incremental battery capacity and voltage during at least partial charging or at least partial discharging of the battery in the past. From the historical battery state curves, extract the curve similarity between each historical battery state curve and the initial battery state curve, and the curve similarity characterizes the difference in similarity between the historical battery state curve and the initial battery state curve; Using the curve similarity as a health indicator, a capacity estimation model is established, which characterizes the calculation relationship between the curve similarity and the current maximum usable capacity of the battery. The capacity estimation model is fitted using historical battery data to determine the model parameters. The historical battery data includes the correspondence between historical battery capacity and historical curve similarity. The capacity estimation model is determined using the following formula: Where Qi is the current maximum available capacity of the battery, A, B, and C are the model parameters of the capacity estimation model, ADi is the curve similarity, and i is the number of charge-discharge cycles.

9. A device for estimating the state of battery health, characterized in that, include: The curve acquisition module is used to acquire the battery state curve, which represents the relationship between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging. The similarity difference module is used to obtain the curve similarity between the battery state curve and the initial state curve. The initial state curve represents the correspondence between the incremental capacity and voltage of the battery during at least partial charging or at least partial discharging in the initial charge-discharge cycle. The curve similarity represents the similarity difference between the battery state curve and the initial state curve. A health status module is used to calculate the battery health status by using the curve similarity as a health indicator. Both the battery state curve and the initial state curve include the correspondence between the incremental capacity and voltage of the battery within a preset SOC range. The preset SOC range is the SOC range including the peak segment of the battery's incremental capacity and voltage curves. Specifically, the health status module uses the curve similarity as a health indicator to calculate the battery health status by: obtaining the battery's current maximum usable capacity based on the curve similarity and a capacity estimation model; and using the ratio of the current maximum usable capacity to the battery's rated capacity as the battery health status. The capacity estimation model is determined by the following formula: Where Qi is the current maximum available capacity of the battery, A, B, and C are the model parameters of the capacity estimation model, ADi is the curve similarity, and i is the number of charge-discharge cycles.

10. A vehicle, characterized in that, include: A controller and a memory; wherein the memory is connected to the controller, the memory is used to store a computer program, and the controller is used to implement the battery health state estimation method as described in any one of claims 1 to 7 by running the computer program stored in the memory.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for estimating battery health status as described in any one of claims 1 to 7 or the method for establishing a capacity estimation model as described in claim 8.

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