Systems and methods for determining battery health and electric vehicles

Abstract

This application discloses a system, method, and electric vehicle for determining battery health, relating to the technical field of battery health detection. The system includes: a data monitoring device and a controller. The data monitoring device determines battery pressure sensing data based on pressure values ​​monitored by pressure sensors within a pressure sensor group. The controller, for any pressure sensor within the pressure sensor group, determines a first extreme value moment when the pressure sensor detects an extreme value among all pressure values; determines a second extreme value moment when a reference sensor detects an extreme value among all pressure values; determines a target time difference between the first and second extreme value moments; determines a target comparison result for each pressure sensor based on the target time difference and a preset threshold; and iterates through all pressure sensors to determine the battery health based on all target comparison results. This addresses the problem of improving the accuracy of battery health detection and enhances the precision of battery health detection.

Description

Technical Field

[0001] This application relates to the technical field of battery health testing, and more specifically, to a system, method, and electric vehicle for determining battery health. Background Technology

[0002] Current battery health assessment strategies rely on macroscopic electrochemical parameters such as capacity retention and DC internal resistance. However, these only reflect overall degradation and cannot identify local defects such as lithium plating, electrode wrinkles, or foil leakage. Moreover, these defects often only become apparent after a significant capacity drop, making early warning difficult. Furthermore, current methods do not consider the physical expansion behavior caused by lithium intercalation in the negative electrode graphite during charging and discharging, and cannot monitor the dynamic response of battery surface stress in real time, making it difficult to accurately perceive the battery's healthy operating status.

[0003] Therefore, in related technologies, no effective solution has yet been proposed for the technical problem of how to improve the accuracy of battery health detection. Summary of the Invention

[0004] This application provides a system, method, and electric vehicle for determining battery health, in order to at least solve the technical problem of how to improve the detection accuracy of battery health in related technologies.

[0005] According to one embodiment of this application, a battery health determination system is provided, comprising: a data monitoring device and a controller, wherein the data monitoring device is electrically connected to the controller; the data monitoring device includes a pressure sensor group attached to the large surface of the battery in the form of a grid array, used to determine the battery's pressure sensing data based on the pressure values ​​monitored by the pressure sensors in the pressure sensor group, wherein each pressure sensor in the pressure sensor group is individually set at a grid point in the grid array, used to collect the pressure value within the grid point; the controller is used to: for any pressure sensor in the pressure sensor group, determine a first extreme value moment when the pressure sensor detects an extreme value among all pressure values; determine a second extreme value moment when a reference sensor detects an extreme value among all pressure values; determine a target time difference between the first extreme value moment and the second extreme value moment; determine a target comparison result for each pressure sensor based on the target time difference and a preset threshold; traverse all pressure sensors and determine the battery health based on all target comparison results; wherein the reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or a reference sensor determined from the central region of the large surface of the battery.

[0006] According to another aspect of the embodiments of this application, a method for determining battery health is also provided, comprising: determining battery pressure sensing data based on pressure values ​​monitored by pressure sensors in a pressure sensor group, wherein each pressure sensor in the pressure sensor group is individually set at a grid point in a grid array to collect pressure values ​​within the grid point; for any pressure sensor in the pressure sensor group, determining a first extreme value moment when the pressure sensor detects an extreme value among all pressure values; determining a second extreme value moment when a reference sensor detects an extreme value among all pressure values; determining a target time difference between the first extreme value moment and the second extreme value moment; determining a target comparison result for each pressure sensor based on the target time difference and a preset threshold; traversing all pressure sensors and determining the battery health based on all target comparison results; wherein the reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or a reference sensor determined from the central region of the battery surface.

[0007] According to another aspect of the embodiments of this application, an electric vehicle is also provided, which is equipped with the above-described battery health determination system.

[0008] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to execute the above-described method for determining battery health when it is run.

[0009] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described method for determining battery health through the computer program.

[0010] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the above-described method for determining battery health.

[0011] This application proposes a battery health determination system including a data monitoring device and a controller. The data monitoring device includes a pressure sensor group attached to the large surface of the battery in the form of a grid array, used to determine the battery's pressure sensing data based on the pressure values ​​monitored by the pressure sensors in the pressure sensor group. Each pressure sensor in the pressure sensor group is individually set at a grid point in the grid array to collect the pressure value within that grid point. The controller is used to: for any pressure sensor in the pressure sensor group, determine a first extreme value moment when the pressure sensor detects an extreme value among all pressure values; determine a second extreme value moment when a reference sensor detects an extreme value among all pressure values; determine a target time difference between the first extreme value moment and the second extreme value moment; determine a target comparison result for each pressure sensor based on the target time difference and a preset threshold; and traverse all pressure sensors to determine the battery health based on all target comparison results. The reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or a reference sensor determined from the central region of the large surface of the battery. By employing the above technical solution, multiple independent pressure sensors are deployed in a grid array on the large surface of the battery to collect pressure distribution data at each grid point on the battery surface in real time. The controller identifies the extreme values ​​among all pressure values ​​and the corresponding times of occurrence of the extreme values, namely the first extreme value time and the second extreme value time. Then, the time difference between the two is calculated as the target time difference. The target time difference is then compared with a preset threshold, thus realizing a solution for actively assessing the health of the battery. Therefore, it can solve the technical problem of how to improve the detection accuracy of battery health in related technologies, improve the detection accuracy of battery health, and enhance the battery safety monitoring capability. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0013] 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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the structure of a battery health determination system according to an embodiment of this application;

[0015] Figure 2 This is a schematic diagram of a large surface of a battery according to an embodiment of this application;

[0016] Figure 3 This is a schematic diagram of a pressure sensor array attached to the large surface of a battery according to an embodiment of this application;

[0017] Figure 4 This is a schematic diagram of the pressure change curve of a reference sensor according to an embodiment of this application;

[0018] Figure 5 This is a schematic diagram illustrating the comparison results of different extreme values ​​according to an embodiment of this application;

[0019] Figure 6 This is a schematic diagram showing the difference between the maximum value time of one sensor and the average value of the maximum value times of all sensors according to an embodiment of this application.

[0020] Figure 7 This is a schematic diagram of pressure value change curves of different sensors according to embodiments of this application;

[0021] Figure 8 This is a schematic diagram (1) of the time difference of different extreme values ​​according to an embodiment of this application;

[0022] Figure 9 This is a schematic diagram (2) of the time difference of different extreme values ​​according to an embodiment of this application;

[0023] Figure 10 This is a schematic diagram (3) of the time difference of different extreme values ​​according to an embodiment of this application;

[0024] Figure 11 This is a schematic diagram (4) of the time difference of different extreme values ​​according to an embodiment of this application;

[0025] Figure 12 This is a schematic diagram (5) of the time difference of different extreme values ​​according to an embodiment of this application;

[0026] Figure 13 This is a schematic diagram (6) of the time difference of different extreme values ​​according to an embodiment of this application;

[0027] Figure 14 This is a schematic diagram (7) of the time difference of different extreme values ​​according to an embodiment of this application;

[0028] Figure 15 This is a schematic diagram of a method for determining battery health according to an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] The following appropriately discloses an embodiment of a battery according to this application. However, unnecessary detailed descriptions may be omitted. For example, detailed descriptions of well-known matters and repetitive descriptions of practically identical structures may be omitted. This is to avoid making the following description unnecessarily lengthy and to facilitate understanding by those skilled in the art. Furthermore, the following description is provided to enable those skilled in the art to fully understand this application and is not intended to limit the subject matter of the claims.

[0032] The battery in this application is a secondary battery, also known as a rechargeable battery or storage battery, which refers to a battery that can be used again after being discharged by recharging to activate the active materials.

[0033] Typically, a secondary battery includes an electrode assembly, an electrolyte, and an outer casing. The electrode assembly consists of a positive electrode, a negative electrode, and a separator. The electrode assembly and electrolyte are assembled inside the outer casing. During charging and discharging, active ions (such as lithium ions) move back and forth between the positive and negative electrodes, inserting and extracting. The separator, positioned between the positive and negative electrodes, primarily prevents short circuits while allowing active ions to pass through. The electrolyte, located between the positive and negative electrodes, mainly serves to conduct active ions.

[0034] This embodiment provides a system for determining battery health, such as... Figure 1 As shown, it includes a data monitoring device 102 and a controller 104, wherein the data monitoring device is electrically connected to the controller;

[0035] It should be noted that by electrically connecting the data monitoring device to the controller, the physical signals collected by the data monitoring device can be directly transmitted to the controller through the circuit path without intermediate conversion or manual intervention. This allows the controller to receive real-time pressure data from the data monitoring device, providing a basic input for subsequent analysis of pressure changes of various grid sensors on the battery surface.

[0036] The data monitoring device includes a pressure sensor group attached to the large surface of the battery in the form of a grid array, used to determine the pressure sensing data of the battery based on the pressure values ​​monitored by the pressure sensors in the pressure sensor group. Each pressure sensor in the pressure sensor group is individually set at a grid point in the grid array, used to collect the pressure value within the grid point.

[0037] It is understandable that the physical expansion behavior caused by lithium intercalation in the graphite of the negative electrode during battery charging and discharging leads to spatiotemporal nonuniformity in different areas of the battery surface, and the temporal difference between the maximum and minimum expansion moments can reflect local structural defects. The battery surface, for example... Figure 2 As shown, the pressure sensor array is further attached in the form of a grid array. Figure 3 The battery surfaces 1 and 2 shown enable precise monitoring of surface pressure distribution. Each pressure sensor is independently positioned at a grid point in the grid array, ensuring that each grid point corresponds to an independent pressure value acquisition point, thus forming a discrete pressure sensing network covering the battery surface. Based on the pressure values ​​monitored by each sensor, the pressure sensor group directly generates pressure sensing data characterizing the stress state at various locations on the battery surface, providing raw measurement data for subsequent analysis of the battery's expansion behavior during charging and discharging.

[0038] The controller is configured to: for any pressure sensor in the pressure sensor group, determine a first extreme moment when the pressure sensor detects an extreme value among all pressure values; determine a second extreme moment when a reference sensor detects an extreme value among all pressure values; determine a target time difference between the first extreme moment and the second extreme moment; determine a target comparison result for each pressure sensor based on the target time difference and a preset threshold; and traverse all pressure sensors to determine the battery health based on all target comparison results; wherein the reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or a reference sensor determined from the central region of the battery surface.

[0039] The controller establishes a time-dimensional correspondence between extreme values ​​detected by different sensors through the above steps. Specifically, it measures the time points when the extreme pressure values ​​of the battery occur during charging and discharging, calculates the time interval between the extreme values ​​of the pressure values ​​of any sensor at any location, and forms a target time difference for that location. Then, based on the direct comparison between the target time difference and a preset threshold, it determines whether the temporal characteristics of the battery's internal stress response are within the allowable range, thereby directly reflecting the periodic consistency of the battery electrode expansion behavior. Furthermore, without additionally relying on capacity or internal resistance changes, the controller determines the battery's health status through the compliance of the time difference.

[0040] Based on the above scheme, a battery health determination system including a data monitoring device and a controller is proposed. The data monitoring device includes a pressure sensor group attached to the large surface of the battery in the form of a grid array, used to determine the battery's pressure sensing data based on the pressure values ​​monitored by the pressure sensors in the pressure sensor group. Each pressure sensor in the pressure sensor group is individually set at a grid point in the grid array to collect the pressure value within that grid point. The controller is used to: for any pressure sensor in the pressure sensor group, determine a first extreme value moment when any pressure sensor detects an extreme value among all pressure values; determine a second extreme value moment when a reference sensor detects an extreme value among all pressure values; determine a target time difference between the first extreme value moment and the second extreme value moment; determine a target comparison result for each pressure sensor based on the target time difference and a preset threshold; and traverse all pressure sensors to determine the battery health based on all target comparison results. The reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or a reference sensor determined from the central region of the large surface of the battery. By employing the above technical solution, multiple independent pressure sensors are deployed in a grid array on the large surface of the battery to collect pressure distribution data at each grid point on the battery surface in real time. The controller identifies the extreme values ​​among all pressure values ​​and the corresponding times of occurrence of the extreme values, namely the first extreme value time and the second extreme value time. Then, the time difference between the two is calculated as the target time difference. The target time difference is then compared with a preset threshold, thus realizing a solution for actively assessing the health of the battery. Therefore, it can solve the technical problem of how to improve the detection accuracy of battery health in related technologies, improve the detection accuracy of battery health, and enhance the battery safety monitoring capability.

[0041] In an exemplary embodiment, the first extreme moment includes a first maximum moment where the extreme value is a maximum value, and the second extreme moment includes a second maximum moment where the extreme value is a maximum value; the target time difference includes a first time difference, the preset threshold includes a first preset threshold, and the target comparison result includes a first comparison result; the controller is further configured to: determine a first time difference between the first maximum moment and the second maximum moment when it is determined that the reference sensor includes the reference sensor, and determine the battery health based on a first comparison result of the first time difference and the first preset threshold. In this embodiment, by setting a reference sensing area in the grid array, by extracting the first time difference between the maximum moment at the location of the first pressure sensor and the maximum moment in the reference sensing area where the second pressure sensor is located, and comparing the first time difference with a preset first preset threshold, the battery health is determined based on the comparison result, thereby determining whether the battery is abnormal and improving the sensitivity of battery health determination.

[0042] In an exemplary embodiment, the controller is further configured to: determine that the battery health is abnormal when the first comparison result indicates that the first time difference is greater than the first preset threshold; and determine that the battery health is normal when the first comparison result indicates that the first time difference is less than or equal to the first preset threshold.

[0043] In this embodiment, by directly comparing the first time difference with a pre-set first preset threshold, when the first time difference is greater than the first preset threshold, the battery health is determined to be abnormal, indicating that internal defects have caused a significant shift in the stress propagation timing; when the time difference is less than or equal to the first preset threshold, the battery health is determined to be normal, indicating that the stress response timing is within the expected range. Thus, the battery health is accurately determined based on the extreme timing difference of the spatially correlated region and the threshold binary logic, improving the accuracy of battery health.

[0044] In an exemplary embodiment, the first extreme moment includes the first minimum moment where the extreme value is a minimum value, and the second extreme moment includes the second minimum moment where the extreme value is a minimum value; the target time difference includes the second time difference, the preset threshold includes the second preset threshold, and the target comparison result includes the second comparison result; the controller is further configured to: determine the second time difference between the first minimum moment and the second minimum moment when it is determined that the reference sensor includes the reference sensor, and determine the health of the battery based on the second comparison result of the second time difference and the second preset threshold.

[0045] In this embodiment, by calculating the second time difference between the first minimum time and the second minimum time and comparing it with a preset second threshold, a second comparison result is obtained. This can capture the phenomenon of delayed or advanced local minimum expansion time caused by defects such as inconsistent expansion of internal electrodes, uneven distribution of electrolyte, or aging of the separator, thereby improving the accuracy of early diagnosis of battery abnormalities.

[0046] In one exemplary embodiment, the controller is further configured to: determine that the battery health is abnormal when the second comparison result indicates that the second time difference is greater than the second preset threshold; and determine that the battery health is normal when the second comparison result indicates that the second time difference is less than or equal to the second preset threshold.

[0047] In this embodiment, the controller determines whether there is any abnormality in the timing of local expansion and contraction caused by lithium plating, electrode wrinkles, or foil leakage inside the battery based on the second comparison result of the second time difference and the second preset threshold. For example, when the second time difference is greater than the second preset threshold, it can indicate that the minimum expansion time of the electrode in different regions is significantly delayed or advanced during the discharge process, thus determining that the battery health is abnormal. Conversely, when the second time difference is less than or equal to the second preset threshold, it indicates that the spatiotemporal consistency of the stress response is good and the battery health is normal, which improves the detection sensitivity of the battery health status and thus improves the accuracy of identifying the risk of early abnormal degradation of the battery.

[0048] In an exemplary embodiment, the controller is further configured to: determine the central region of the large surface of the battery, wherein the number of central sensors in the central region is determined based on a preset number of rows and a preset number of columns, both of the preset number of rows and the preset number of columns being greater than a first preset value, and the sum of the preset number of rows and the preset number of columns being greater than a second preset value, the first preset value being less than the second preset value; and determine the reference sensor based on the central sensors in the central region that satisfy reference conditions.

[0049] In this embodiment, a central region defined by a preset number of rows and columns is delineated on the large surface of the battery. Both the number of rows and columns are ensured to be greater than a first preset value to cover a sufficiently stable pressure response area. Simultaneously, the sum of the number of rows and columns is limited to a second preset value to prevent the region from becoming too large and introducing edge interference. This spatially constrains the selection range of the reference sensor. Based on this, the controller selects sensors that meet preset reference conditions from within the central region as reference sensors. This reduces misjudgments of extreme time deviations caused by selecting edge or unsteady-state sensors, improves the accuracy of the target time difference, and thus enhances the reliability of health assessment.

[0050] Optionally, in one embodiment, if no reference sensor satisfying the reference conditions is determined from the central region, a reference sensor satisfying the reference conditions is re-determined from a candidate region on the large surface of the battery, wherein the candidate region represents the area on the large surface of the battery other than the central region, and the number of candidate sensors in the candidate region is determined based on a preset number of rows and a preset number of columns.

[0051] In an exemplary embodiment, the controller is further configured to: determine a first monitoring time when each central sensor detects an extreme value among all pressure values; and for different first monitoring times corresponding to all central sensors, if it is determined that the third time difference between the different first monitoring times is less than the preset threshold, select the central sensor corresponding to the third time difference with the smallest index difference as the reference sensor, or select the central sensor corresponding to the third time difference with the smallest difference from the preset average value as the reference sensor.

[0052] In this embodiment, the difference between the index values ​​of the central sensor or the preset average value is used as the screening criterion. When the difference between the pressure extreme values ​​monitored by the central sensor at the third moment is less than the preset threshold, the central sensor with the smallest corresponding index difference or the smallest deviation from the preset average value is automatically selected as the reference point. This can reduce the probability of misselection caused by local sudden changes or noise and improve the accuracy of selecting the reference sensor.

[0053] Optionally, the aforementioned preset average value can be understood as the average of all third-time differences, or it can be the average of the third-time differences of sensors other than the current central sensor. For example, in a central region including N×M central sensors, for each central sensor, the average value corresponding to the difference between the monitoring time when each central sensor detects the pressure extreme value and the monitoring time when the remaining (N×M-1) sensors detect the pressure extreme value (corresponding to the third-time differences) is compared sequentially, and the sensor closest to the average value is selected as the reference sensor.

[0054] Optionally, for the above-mentioned index difference, for example, representing the sum of absolute values ​​or the sum of squares of the differences at the third time point, in the central region including N×M central sensors, for each central sensor, the index difference corresponding to the difference between the monitoring time when each central sensor detects the pressure extreme value and the monitoring time when the remaining (N×M-1) sensors detect the pressure extreme value (corresponding to the difference at the third time point) is compared, that is, the sum of absolute values ​​or the sum of squares of the differences are compared, and the sensor with the smallest index difference is selected as the reference sensor.

[0055] In an exemplary embodiment, the controller is further configured to: determine the pressure difference of all pressure values ​​monitored by each central sensor; for multiple pressure differences corresponding to all central sensors, if it is determined that all multiple pressure differences are less than or equal to a preset pressure threshold, determine the average of the multiple pressure differences; calculate the difference between the average and the pressure differences traversed to obtain the sum of the squares of the differences, and determine the reference sensor based on the central sensor corresponding to the minimum value among the multiple sums of squares.

[0056] In this embodiment, when all pressure differences do not exceed a preset pressure threshold, the sum of the squares of the pressure differences of each central sensor and the overall average is calculated, and the sensor with the smallest sum of squares is selected as the reference sensor, thereby improving the accuracy of the reference sensor and reducing the probability of evaluation fluctuations and misjudgments caused by fuzzy reference point selection or reliance on human experience.

[0057] Optionally, the different extreme values ​​mentioned above may specifically include different maximum values ​​and different minimum values.

[0058] Optionally, each time the central sensor is traversed, a squared cumulative value can be calculated, and multiple squared cumulative values ​​represent the values ​​obtained by traversing all the central sensors.

[0059] In one exemplary embodiment, the controller is further configured to: determine a second monitoring time when the other sensors detect an extreme value among all pressure values, provided that the reference sensor includes the other sensors; determine the second extreme value time based on the second monitoring time; or, for all second monitoring times corresponding to at least two other sensors, determine the second extreme value time based on the average value of all second monitoring times.

[0060] In this embodiment, for a single other sensor, the second extreme moment is determined directly based on the second monitoring moment; for at least two other sensors, the second extreme moment is determined based on the average of all corresponding second monitoring moments. Different calculation methods can be provided according to the number of other sensors, thereby improving the accuracy of the second extreme moment.

[0061] In an exemplary embodiment, the controller is further configured to: when the extreme value is a maximum value, determine a third maximum value time in the first extreme value time, and determine a fourth maximum value time in the second extreme value time; determine a fourth time difference between the third maximum value time and the fourth maximum value time, and determine the health of the battery based on a third comparison result of the fourth time difference and a third preset threshold.

[0062] Optionally, if the third comparison result indicates that the difference at the fourth time point is greater than the third preset threshold, the battery health is determined to be abnormal; if the third comparison result indicates that the difference at the fourth time point is less than or equal to the third preset threshold, the battery health is determined to be normal.

[0063] For example, if during the third charge-discharge cycle, the maximum value of the sensor in the fourth row and third column is 33500s, while that of the reference sensor (second row and sixth column) is 32900s, the time difference is 600s, which does not exceed the threshold of 1200s. Therefore, it is determined that there is no abnormality; otherwise, it is an abnormality and an early warning is triggered.

[0064] In an exemplary embodiment, the controller is further configured to: determine a third minimum moment in the first extreme moment and a fourth minimum moment in the second extreme moment when the extreme value is a minimum moment; determine a fifth moment difference between the third minimum moment and the fourth minimum moment; and determine the health of the battery based on a fourth comparison result of the fifth moment difference and a fourth preset threshold.

[0065] Optionally, if the fourth comparison result indicates that the difference at the fifth time point is greater than the fourth preset threshold, the battery health is determined to be abnormal; if the fourth comparison result indicates that the difference at the fifth time point is less than or equal to the fourth preset threshold, the battery health is determined to be normal.

[0066] Through the above embodiments, the extreme value time corresponding to the current pressure sensor that exhibits an extreme value in the grid array pressure sensor group is identified as the first extreme value time. Under the premise that the reference sensor is another sensor, the monitoring time corresponding to the pressure extreme value detected by other sensors is further extracted. The second extreme value time is determined based on the monitoring time or the average value of these times is calculated as the second extreme value time. The battery health is quantitatively assessed by comparing the target time difference between the first extreme value time and the second extreme value time with a preset threshold. This reduces the error caused by relying on the local extreme value of a single sensor, improves the reliability of battery health detection, and enables early warning of abnormal battery conditions.

[0067] In an exemplary embodiment, the controller is further configured to: determine that the battery health is abnormal if a first ratio of the target time difference to the preset charging duration is greater than a fifth preset threshold; and determine that the battery health is normal if the first ratio is less than or equal to the fifth preset threshold.

[0068] In an exemplary embodiment, the controller is further configured to: determine that the battery health is abnormal if a second ratio of the target time difference to the preset discharge duration is greater than a sixth preset threshold; and determine that the battery health is normal if the second ratio is less than or equal to the sixth preset threshold.

[0069] In this embodiment, by proportionally calculating the target time difference with preset charging and discharging times to form a first ratio and a second ratio, respectively, when either the first or second ratio exceeds its respective preset threshold, it indicates a significant inconsistency or defect within the battery causing an expansion timing mismatch, thus determining an abnormal battery health. Conversely, if the ratio falls within the threshold, the battery is considered normal. Compared to traditional methods that rely solely on absolute time thresholds, this method quantifies the relative timing deviation caused by the lag in local electrode expansion into a proportional characteristic relative to the charge / discharge cycle. This allows for sensitive detection of battery anomalies, enabling early warning and improving the accuracy and reliability of battery health status assessment.

[0070] In an exemplary embodiment, the controller is further configured to: determine that the battery health is abnormal if the absolute value of the target time difference is continuously increasing during the monitoring period; or, determine that the battery health is abnormal if the absolute value of the target time difference is continuously increasing during the monitoring period and the rate of increase is greater than a growth rate threshold.

[0071] Optionally, if the absolute value of the target time difference does not continuously increase during the monitoring period, other methods in the above embodiments can be used to further determine whether the battery is abnormal.

[0072] In this embodiment, the controller can monitor the changing trend of the absolute value of the target time difference over a continuous monitoring period. When this absolute value shows a continuous increasing trend, it indicates that the battery has a latent defect or a tendency to degrade. Furthermore, when the rate of increase exceeds a preset growth threshold, it indicates that the deterioration process of stress distribution differences is accelerating, reflecting that the battery health status is changing from potential risk to significant deterioration, at which point the battery health is abnormal. This embodiment, by introducing a judgment condition for the dynamic evolution trend of the target time difference, can determine battery abnormalities in advance before the target time difference reaches the traditional alarm limit, effectively improving the timeliness and accuracy of battery safety monitoring.

[0073] In an exemplary embodiment, the controller is further configured to: determine the presence of abnormal pressure sensors, wherein the target comparison result of the abnormal pressure sensors is used to indicate that the target time difference is greater than the preset threshold; determine that the battery health is abnormal if the number of abnormal pressure sensors is greater than the preset number; and determine that the battery health is abnormal if the proportion of the number of abnormal pressure sensors in the total number of all pressure sensors is greater than the first preset ratio.

[0074] In one exemplary embodiment, the controller is further configured to: determine that the battery health is abnormal if the number of times the abnormal pressure sensor malfunctions is greater than a preset number; and determine that the battery health is abnormal if the proportion of the number of times the abnormal pressure sensor malfunctions in the total number of monitoring times of all pressure sensors is greater than a second preset ratio.

[0075] The above embodiments provide different ways to determine the health of the battery. For example, abnormal pressure sensors can be identified directly based on the target time difference being greater than a preset threshold, reducing the probability of false alarms from a single sensor. Alternatively, if the number of abnormal pressure sensors exceeds a preset number, or the proportion of abnormal pressure sensors exceeds a first preset ratio, or the same sensor is abnormal multiple times (more than a preset number of times), or the proportion of its abnormal frequency exceeds a second preset ratio, then the sensor is abnormal. The abnormal condition of the sensor can be used to further determine the battery health and improve the reliability of early warning.

[0076] Optionally, for example, in this embodiment, if the accuracy of each sensor in monitoring pressure is greater than a preset accuracy, it is considered to have high reliability. In this case, a single abnormality of a single sensor is directly determined to be a battery malfunction. For other situations, such as more than one sensor experiencing a single abnormality, or at least one sensor experiencing multiple abnormalities, or at least one sensor experiencing abnormalities at a frequency or with an abnormal value ratio exceeding a certain range, the battery is determined to be malfunctioning.

[0077] To better understand the process of determining battery health, the implementation flow of the above-mentioned battery health determination method will be described below in conjunction with optional embodiments, but this is not intended to limit the technical solution of the embodiments of this application.

[0078] The following steps will further explain the battery health diagnosis process.

[0079] Step 1: Using a gridded pressure sensor array, measure the pressure values ​​of each grid sensor on the large surface of the battery under different operating conditions, such as resting, charging, and discharging. For example, Figure 7As shown, taking a sensor located at grid points such as (2.5) as an example, a graph of the pressure values ​​monitored over time is provided.

[0080] Step 2: Analyze the pressure values ​​P of each sensor during each charge-discharge cycle. i，j The maximum value at time t i，j，max and the minimum time t i，j，min .

[0081] Among them, P i，j t represents the pressure value of the sensor in the i-th row and j-th column. i，j，max The pressure value P represents the sensor in the i-th row and j-th column. i，j The time point at which the maximum value is reached. t i，j，min The pressure value P represents the sensor in the i-th row and j-th column. i，j The time point at which the minimum value is reached. Figure 4 A graph showing the change of sensor pressure value over time is provided, with the maximum value at time such as... Figure 4 The red extreme points shown indicate the minimum values ​​at the following times: Figure 4 The green extreme points are shown.

[0082] like Figure 6 As shown, a heatmap is used to illustrate the times of maximum values ​​monitored by the grid sensors located on the large surface of the battery. The difference between these extreme values ​​is then calculated using the following method.

[0083] 1. The average value t of the time when the pressure maxima occur with other sensors (belonging to other sensing areas) mean，max Or the time t when the pressure maximum occurs at the reference sensor (belonging to the reference sensing region). ref，max Compare and calculate the time difference at the maximum value (corresponding to the difference at the first time step) Δt. i，j，max Δt ref，max like Figure 5 As shown.

[0084] Where, Δt i，j，max =t i，j，max -t mean，max Or, Δt i，j，max =t i，j，max -t ref，max .

[0085] 2. The average value t of the time when the pressure minimum occurs compared with other sensors. mean，min Or the time t when the pressure of the reference sensor reaches its minimum. ref，min Compare and calculate the time difference Δt for the minimum value. i，j，min Δt ref，min like Figure 5 As shown.

[0086] Where, Δti，j，min =t i，j，min -t mean，min , or Δt i，j，min =t i，j，min -t ref，min .

[0087] Optionally, in step 2, a method for determining the moments of pressure maxima and minima is further proposed. First, the pressure values ​​are smoothed using a sliding window. For any pressure value at a grid point where a sensor is located, if it is greater than the values ​​at P1 points to its left and P1 points to its right, and the difference is greater than a threshold, then this pressure value is determined to be a maxima, and the corresponding moment is the maxima moment t. max .

[0088] If the pressure value is less than the values ​​at P1 points to its left and P1 points to its right, and the absolute value of the difference is greater than the threshold, then this pressure value is determined to be a minimum, and the corresponding time is the minimum time t. min .

[0089] Among them, P1 can be selected according to the sampling period, such as 20, 50, 100, 200, 300, etc., and the threshold can be selected as 10-1000 times the sampling accuracy of the pressure sensor.

[0090] Optionally, in step 2, a method for determining the reference sensor is further proposed.

[0091] Specifically, select N×M sensors belonging to the central area from the large surface of the battery, where N≥1, M≥1, and N+M≥3. Select reference sensors within the central area according to reference conditions. Alternatively, if no sensor in the central area meets the reference conditions, select other N×M areas from the large surface of the battery and continue to select reference sensors within other N×M areas according to reference conditions.

[0092] The reference conditions include one of the following.

[0093] 1. During at least two charge-discharge cycles, the time difference between the maximum values ​​of the N×M sensors does not exceed a set threshold Δt. threshold，max .

[0094] During at least two charge-discharge cycles, the minimum time difference of the N×M sensors does not exceed a set threshold Δt. threshold，min .

[0095] 2. For each sensor, sequentially compare the time difference between the time each sensor detects the pressure extreme value and the time of the remaining (N×M-1) sensors detecting the pressure extreme value, and select the sensor with the smallest index difference as the reference sensor. Alternatively, sequentially compare the average value of the time difference between the time each sensor detects the pressure extreme value and the time of the remaining (N×M-1) sensors detecting the pressure extreme value, and select the sensor closest to the average value as the reference sensor.

[0096] 3. During at least two charge-discharge cycles, the pressure difference among the N×M sensors never exceeds the set threshold ΔP. threshold To further determine the mean of the pressure extreme values ​​detected by N×M sensors, calculate the sum of the squares of the pressure difference between each sensor and the mean, and determine the sensor with the smallest sum as the reference sensor.

[0097] Optionally, in step 2, a method for updating the reference sensor according to the following conditions is further proposed.

[0098] 1. When the cumulative charge-discharge capacity of the battery reaches C1 times its initial capacity, the reference sensor is updated. C1 can be set according to the battery life, for example, selecting 50, 100, 200, 500, etc. Ah is an abbreviation for ampere-hour, a unit for measuring battery charge capacity. 1 Ah represents the amount of charge transferred by a current of 1 ampere continuously discharging for 1 hour (h). If a battery has a nominal capacity of 50 Ah, it can discharge at a current of 50 A for 1 hour, or at a current of 5 A for 10 hours.

[0099] 2. If the battery's cumulative operating time exceeds C2 hours after the last selected reference sensor location, the reference sensor will be updated. C2 can be selected based on the battery's service life, such as 0.5, 1, or 2 years.

[0100] Step 3: Anomaly Detection. The anomaly detection process is illustrated in the following examples.

[0101] In one embodiment, the time difference Δt between the maximum values ​​of each sensor is determined. i，j，max Does the absolute value exceed the set threshold Δt? threshold，max If the threshold is exceeded, it is considered abnormal; otherwise, it is considered normal; and / or the time difference Δt between the minimum values ​​of each sensor is determined. i，j，min Does the absolute value exceed Δt? threshold，min If the threshold is exceeded, it is considered abnormal; otherwise, it is considered normal.

[0102] In one embodiment, the time difference Δt between the maximum values ​​of each sensor is determined.i，j，max The ratio Δt between the absolute value and the total charging time tcharge ri，j，max Does it exceed the threshold Δt? rthreshold，max If the threshold is exceeded, it is considered abnormal; otherwise, it is considered normal.

[0103] And / or, determine the time difference Δt between the minimum values ​​of each sensor. i，j，min The ratio Δt between the absolute value of the discharge time tdischarge and the total discharge time tdischarge ri，j，min Does it exceed the set threshold Δt? rthreshold，min If the threshold is exceeded, it is considered abnormal; otherwise, it is considered normal.

[0104] In one embodiment, if Δt i，j，max or Δt i，j，min A continuously increasing absolute value can also be considered abnormal. Among them, Δt i，j，max or Δt i，j，min This corresponds to the target time difference mentioned above.

[0105] One approach is to conduct full lifecycle testing on a batch of healthy batteries, statistically analyze the distribution range of extreme time differences obtained from the tests, and take the average value within this range as the normal range for extreme values. Alternatively, an error value (such as ±3) can be added to the average value to define the normal range for extreme values. Another approach is to set a threshold Δt based on the battery extreme values ​​supported by different battery material systems. threshold，max and Δt threshold，min .

[0106] Optionally, in one embodiment, the variance or standard deviation of the time difference of the sensor extreme values ​​can be used as a health indicator, and whether the sensor is abnormal can be determined based on whether the abnormal threshold of the indicator is exceeded.

[0107] If a single sensor has high reliability, a single sensor malfunction can directly indicate a battery malfunction. If more than one sensor experiences a single malfunction, the battery is considered faulty. If at least one sensor experiences multiple malfunctions, or if the frequency of at least one sensor malfunctions exceeds a certain range, the battery is considered faulty.

[0108] Optionally, in one embodiment, the battery undergoes three charge-discharge cycles, selecting rows 2-3 and columns 5-7. The sensor area consists of three sensors. First, a reference sensor is selected within this area.

[0109] As shown in Tables 1-3, the maximum time difference between the maximum values ​​was 990 s during the three charge-discharge cycles, which is less than the set threshold Δt. threshold，max (For 1200s), therefore this sensor area meets the reference conditions.

[0110] Table 1

[0111]

[0112] Table 2

[0113]

[0114] Table 3

[0115]

[0116] Table 1 shows the time of the maximum pressure during the first charge-discharge cycle, with a time difference of 990 s. Table 2 shows the time of the maximum pressure during the second charge-discharge cycle, with a time difference of 650 s. Table 3 shows the time of the maximum pressure during the third charge-discharge cycle, with a time difference of 870 s.

[0117] Next, referring to Tables 4-8, it can be seen that during each charging cycle, the difference between the maximum pressure value of each sensor and the average of the maximum pressure values ​​of the six sensors is calculated, and the absolute value is taken. Then, the absolute values ​​obtained from the three cycles are added together. The value of the sensor in the 3rd row and 6th column is the smallest. Therefore, the position in the 3rd row and 6th column is selected as the position of the reference sensor.

[0118] Table 4

[0119]

[0120] Table 5

[0121]

[0122] Table 6

[0123]

[0124] Table 7

[0125]

[0126] Table 8

[0127]

[0128] Table 4 shows the difference between the pressure maximum and the mean during the first charge-discharge cycle; Table 5 shows the difference between the pressure maximum and the mean during the second charge-discharge cycle; Table 6 shows the difference between the pressure maximum and the mean during the third charge-discharge cycle; Table 7 shows the sum of the absolute values ​​of the differences between the pressure maximum and the mean during the three charge-discharge cycles; and Table 8 shows the sum of the squares of the differences between the pressure maximum and the mean during the three charge-discharge cycles.

[0129] Optionally, in one embodiment, the battery undergoes 3 charge-discharge cycles, selecting rows 2-3 and columns 5-7. The sensor area comprised of three sensors is used to first select a reference sensor within this area. For example, if the maximum pressure difference in this sensor area is 144, which is less than the set threshold ΔP... threshold If the value is 200, then this sensor region satisfies the reference condition.

[0130] According to Tables 9 and 10, the sensor with the smallest value is the one whose pressure value is the difference between each sensor and the average pressure value of the six sensors during the three charge-discharge cycles. The absolute values ​​of these differences are then summed. Alternatively, the sensor with the smallest value is the one whose pressure value is the difference between each sensor and the average pressure value of the six sensors during the three charge-discharge cycles. This sensor is then the reference sensor.

[0131] Table 9

[0132]

[0133] Table 10

[0134]

[0135] Table 9 shows the cumulative absolute values ​​of the differences between each pressure sensor and the mean during the three charge-discharge cycles. Table 10 shows the cumulative squared values ​​of the differences between each pressure sensor and the mean during the three charge-discharge cycles.

[0136] Optionally, in one embodiment, the battery undergoes three charge-discharge cycles. Following the aforementioned method, the sensor in the second row and sixth column can be selected as a reference sensor. During each charge-discharge cycle, the difference between the time of maximum pressure of each sensor and the time of maximum pressure of the reference position is calculated, wherein the maximum value of the time difference is taken as the maximum absolute value. Figure 8 The chart shows the time difference between the maximum values ​​obtained during the first charge-discharge cycle. It can be seen that during the first charge, the time difference between the maximum value of the sensor in the third row and third column and the maximum value of the reference sensor is the largest, but it does not exceed the threshold of 1200 seconds. Similarly, Figure 9 The maximum time difference obtained during the second charge-discharge cycle is shown. During the second charge, the maximum time difference of the sensor in the third row and first column did not exceed the threshold of 1200s. Figure 10 The maximum time difference is shown for the third charge-discharge cycle. During the third charge, the maximum time difference of the sensor in row 4, column 3 did not exceed the threshold of 1200s. Therefore, it can be determined that the battery is normal.

[0137] Optionally, in one embodiment, combined with Figures 11-12 This shows the time difference between the maximum values ​​of the battery during the charge-discharge cycle. Among them, Figure 11 This shows the time difference of the maximum value obtained in the first charge-discharge cycle. Figure 12 The maximum time difference obtained during the second charge-discharge cycle is shown.

[0138] According to t i，j，max and t mean，max Calculate Δt i，j，max For example, each Δt i，j，max If the absolute value does not exceed the set threshold of 1200s, then no abnormality is determined.

[0139] Optionally, in one embodiment, combined with Figures 13-14 This shows the time difference between the maximum values ​​of the battery during the charge-discharge cycle. Among them, Figure 13 This shows the time difference of the maximum value obtained in the first charge-discharge cycle. Figure 14 The maximum time difference obtained during the second charge-discharge cycle is shown. Based on t i，j，max and t mean，max Calculate Δt i，j，max For example, each Δt i，j，max If the absolute value exceeds the threshold of 1200 seconds, it is considered abnormal.

[0140] During the first and second charging processes, the Δt of the 11th column sensor... i，j，max If the threshold is exceeded, it indicates that the time for the edge electrode to reach the maximum lithium insertion state is significantly delayed compared to other positions, indicating a serious inconsistency inside the battery and a potential risk of accelerated degradation, thus it is judged as abnormal.

[0141] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0142] Furthermore, in one embodiment, a method for determining battery health is provided, such as... Figure 15 As shown, it specifically includes:

[0143] Step S1502: Determine the battery pressure sensing data based on the pressure values ​​monitored by the pressure sensors in the pressure sensor group, wherein each pressure sensor in the pressure sensor group is individually set at a grid point in the grid array to collect the pressure value within the grid point.

[0144] Step S1504: For any pressure sensor in the pressure sensor group, determine the first extreme value moment when the pressure sensor detects the extreme value among all pressure values; determine the second extreme value moment when the reference sensor detects the extreme value among all pressure values; determine the target time difference between the first extreme value moment and the second extreme value moment; determine the target comparison result of each pressure sensor based on the target time difference and a preset threshold; traverse all pressure sensors and determine the battery health based on all target comparison results; wherein, the reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or a reference sensor determined from the central region of the battery surface.

[0145] Through the above steps, the pressure sensing data of the battery is determined based on the pressure values ​​monitored by the pressure sensors in the pressure sensor group, wherein each pressure sensor in the pressure sensor group is individually set at a grid point in the grid array to collect the pressure value within that grid point; and for any pressure sensor in the pressure sensor group, a first extreme value moment is determined when the pressure sensor detects the extreme value among all pressure values; a second extreme value moment is determined when the reference sensor detects the extreme value among all pressure values; a target time difference between the first extreme value moment and the second extreme value moment is determined; a target comparison result for each pressure sensor is determined based on the target time difference and a preset threshold; and the health of the battery is determined by traversing all pressure sensors and based on all target comparison results; wherein the reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or a reference sensor determined from the central region of the battery surface. By employing the above technical solution, multiple independent pressure sensors are deployed in a grid array on the large surface of the battery to collect pressure distribution data at each grid point on the battery surface in real time. The controller identifies the extreme values ​​among all pressure values ​​and the corresponding times of occurrence of the extreme values, namely the first extreme value time and the second extreme value time. Then, the time difference between the two is calculated as the target time difference. The target time difference is then compared with a preset threshold, thus realizing a solution for actively assessing the health of the battery. Therefore, it can solve the technical problem of how to improve the detection accuracy of battery health in related technologies, improve the detection accuracy of battery health, and enhance the battery safety monitoring capability.

[0146] In an exemplary embodiment, the first extreme moment includes a first maximum moment where the extreme value is a maximum value, and the second extreme moment includes a second maximum moment where the extreme value is a maximum value; the target time difference includes a first time difference, the preset threshold includes a first preset threshold, and the target comparison result includes a first comparison result; when it is determined that the reference sensor includes the reference sensor, a first time difference between the first maximum moment and the second maximum moment is determined, and the health of the battery is determined based on a first comparison result of the first time difference and the first preset threshold.

[0147] In one exemplary embodiment, if the first comparison result indicates that the first time difference is greater than the first preset threshold, the battery health is determined to be abnormal; if the first comparison result indicates that the first time difference is less than or equal to the first preset threshold, the battery health is determined to be normal.

[0148] In an exemplary embodiment, the first extreme moment includes the first minimum moment where the extreme value is the minimum value, and the second extreme moment includes the second minimum moment where the extreme value is the minimum value; the target time difference includes the second time difference, the preset threshold includes the second preset threshold, and the target comparison result includes the second comparison result; when it is determined that the reference sensor includes the reference sensor, the second time difference between the first minimum moment and the second minimum moment is determined, and the health of the battery is determined based on the second comparison result of the second time difference and the second preset threshold.

[0149] In one exemplary embodiment, if the second comparison result indicates that the second time difference is greater than the second preset threshold, the battery health is determined to be abnormal; if the second comparison result indicates that the second time difference is less than or equal to the second preset threshold, the battery health is determined to be normal.

[0150] In an exemplary embodiment, the central region of the large surface area of ​​the battery is determined, wherein the number of central sensors in the central region is determined based on a preset number of rows and a preset number of columns, both of which are greater than a first preset value, and the sum of the preset number of rows and the preset number of columns is greater than a second preset value, wherein the first preset value is less than the second preset value; and a reference sensor is determined based on the central sensors in the central region that satisfy reference conditions.

[0151] In one exemplary embodiment, a first monitoring time is determined when each central sensor detects the extreme value among all pressure values; for different first monitoring times corresponding to all central sensors, if the difference between the third time values ​​of the different first monitoring times is less than the preset threshold, the central sensor corresponding to the third time value with the smallest index difference is selected as the reference sensor, or the central sensor corresponding to the third time value with the smallest difference from the preset average value is selected as the reference sensor.

[0152] In one exemplary embodiment, the pressure difference of all pressure values ​​monitored by each central sensor is determined; for multiple pressure differences corresponding to all central sensors, if it is determined that all multiple pressure differences are less than or equal to a preset pressure threshold, the average of the multiple pressure differences is determined; the difference between the average and the pressure differences traversed is calculated to obtain the sum of the squares of the differences, and the reference sensor is determined according to the central sensor corresponding to the minimum value among the multiple sums of squares.

[0153] In one exemplary embodiment, if it is determined that the reference sensor includes the other sensors, a second monitoring time is determined when the other sensors detect an extreme value among all pressure values; the second extreme value time is determined based on the second monitoring time; or, for all second monitoring times corresponding to at least two other sensors, the second extreme value time is determined based on the average value of all the second monitoring times.

[0154] In an exemplary embodiment, when the extreme value is a maximum value, a third maximum value time in the first extreme value time is determined, and a fourth maximum value time in the second extreme value time is determined; a fourth time difference between the third maximum value time and the fourth maximum value time is determined, and the health of the battery is determined based on a third comparison result of the fourth time difference and a third preset threshold.

[0155] In an exemplary embodiment, when the extreme value is a minimum value, a third minimum value time in the first extreme value time is determined, and a fourth minimum value time in the second extreme value time is determined; a fifth time difference between the third minimum value time and the fourth minimum value time is determined, and the health of the battery is determined based on a fourth comparison result of the fifth time difference and a fourth preset threshold.

[0156] In one exemplary embodiment, if the first ratio of the target time difference to the preset charging time is greater than a fifth preset threshold, the battery health is determined to be abnormal; if the first ratio is less than or equal to the fifth preset threshold, the battery health is determined to be normal.

[0157] In one exemplary embodiment, if the second ratio of the target time difference to the preset discharge duration is greater than a sixth preset threshold, the battery health is determined to be abnormal; if the second ratio is less than or equal to the sixth preset threshold, the battery health is determined to be normal.

[0158] In one exemplary embodiment, if it is determined that the absolute value of the target time difference is continuously increasing within the monitoring period, the battery health is determined to be abnormal; or, if it is determined that the absolute value of the target time difference is continuously increasing within the monitoring period, and the rate of increase is greater than a growth rate threshold, the battery health is determined to be abnormal.

[0159] In one exemplary embodiment, an abnormal pressure sensor can be identified, wherein the target comparison result of the abnormal pressure sensor is used to indicate that the target time difference is greater than the preset threshold; if the number of abnormal pressure sensors is determined to be greater than the preset number, the battery health is determined to be abnormal; if the proportion of the number of abnormal pressure sensors in the total number of all pressure sensors is determined to be greater than the first preset ratio, the battery health is determined to be abnormal.

[0160] In one exemplary embodiment, if the number of times the abnormal pressure sensor malfunctions is greater than a preset number, the battery health is determined to be abnormal; if the proportion of the number of times the abnormal pressure sensor malfunctions in the total number of monitoring times of all pressure sensors is greater than a second preset ratio, the battery health is determined to be abnormal.

[0161] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.

[0162] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:

[0163] S1, determine the battery pressure sensing data based on the pressure value monitored by the pressure sensor in the pressure sensor group, wherein each pressure sensor in the pressure sensor group is individually set at a grid point in the form of a grid array, and is used to collect the pressure value in the grid point;

[0164] S2, for any pressure sensor in the pressure sensor group, determine the first extreme value moment when the pressure sensor detects the extreme value among all pressure values; determine the second extreme value moment when the reference sensor detects the extreme value among all pressure values; determine the target time difference between the first extreme value moment and the second extreme value moment; determine the target comparison result of each pressure sensor based on the target time difference and a preset threshold; traverse all pressure sensors and determine the battery health based on all target comparison results; wherein, the reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or, a reference sensor determined from the central region of the battery surface.

[0165] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0166] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0167] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0168] S1, determine the battery pressure sensing data based on the pressure value monitored by the pressure sensor in the pressure sensor group, wherein each pressure sensor in the pressure sensor group is individually set at a grid point in the form of a grid array, and is used to collect the pressure value in the grid point;

[0169] S2, for any pressure sensor in the pressure sensor group, determine the first extreme value moment when the pressure sensor detects the extreme value among all pressure values; determine the second extreme value moment when the reference sensor detects the extreme value among all pressure values; determine the target time difference between the first extreme value moment and the second extreme value moment; determine the target comparison result of each pressure sensor based on the target time difference and a preset threshold; traverse all pressure sensors and determine the battery health based on all target comparison results; wherein, the reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or, a reference sensor determined from the central region of the battery surface.

[0170] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0171] Optionally, embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0172] Optionally, embodiments of this application also provide an electric vehicle equipped with a battery health determination system as described in any of the above embodiments.

[0173] Optionally, embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0174] Optionally, embodiments of this application also provide a computer program that includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in any of the above method embodiments.

[0175] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0176] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuits, or multiple modules or steps can be fabricated as a single integrated circuit. Thus, this application is not limited to any particular hardware and software combination.

[0177] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A system for determining state of health of a battery, the system comprising: include: A data monitoring device and a controller, wherein the data monitoring device is electrically connected to the controller; The data monitoring device includes a pressure sensor group attached to the large surface of the battery in the form of a grid array, used to determine the pressure sensing data of the battery based on the pressure values ​​monitored by the pressure sensors in the pressure sensor group. Each pressure sensor in the pressure sensor group is individually set at a grid point in the grid array, used to collect the pressure value within the grid point. The controller is used for: For any pressure sensor in the pressure sensor group, determine the first extreme moment when any pressure sensor detects the extreme value among all pressure values; Determine the second extreme moment when the reference sensor detects the extreme value among all pressure values; Determine the target time difference between the first extreme time and the second extreme time; Based on the target time difference and the preset threshold, the target comparison result of each pressure sensor is determined; By iterating through all pressure sensors, the health of the battery is determined based on the comparison results of all targets. The reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or a reference sensor determined from the central region of the battery surface.

2. The battery health determination system according to claim 1, characterized in that, The first extreme moment includes the first maximum moment where the extreme value is the maximum value, and the second extreme moment includes the second maximum moment where the extreme value is the maximum value; the target time difference includes the first time difference, the preset threshold includes the first preset threshold, and the target comparison result includes the first comparison result; the controller is further configured to: If the reference sensor is determined to include the reference sensor, a first time difference between the first maximum time and the second maximum time is determined, and the health of the battery is determined based on a first comparison result of the first time difference and a first preset threshold.

3. The battery health determination system according to claim 2, characterized in that, The controller is also used for: If the first comparison result is determined to indicate that the first time difference is greater than the first preset threshold, the health of the battery is determined to be abnormal. If the first comparison result indicates that the first time difference is less than or equal to the first preset threshold, the battery health is determined to be normal.

4. The battery health determination system according to claim 1, characterized in that, The first extreme value moment includes the first minimum value moment where the extreme value is the minimum value, and the second extreme value moment includes the second minimum value moment where the extreme value is the minimum value; the target time difference includes the second time difference, the preset threshold includes the second preset threshold, and the target comparison result includes the second comparison result; the controller is further configured to: If the reference sensor is determined to include the reference sensor, a second time difference between the first minimum time and the second minimum time is determined, and the health of the battery is determined based on a second comparison result of the second time difference and a second preset threshold.

5. The battery health determination system according to claim 4, characterized in that, The controller is also used for: If the second comparison result indicates that the second time difference is greater than the second preset threshold, the battery health is determined to be abnormal. If the second comparison result indicates that the second time difference is less than or equal to the second preset threshold, the battery's health is determined to be normal.

6. The battery health determination system according to claim 2 or 4, characterized in that, The controller is also used for: The central region of the large surface area of ​​the battery is determined, wherein the number of central sensors in the central region is determined based on a preset number of rows and a preset number of columns, both of which are greater than a first preset value, and the sum of the preset number of rows and the preset number of columns is greater than a second preset value, wherein the first preset value is less than the second preset value; The reference sensor is determined based on the central sensor in the central region that meets the reference conditions.

7. The battery health determination system according to claim 6, characterized in that, The controller is also used for: Determine the first monitoring moment when each central sensor detects the extreme value among all pressure values; For all the different first monitoring times corresponding to the central sensors, if the difference between the third time times between the different first monitoring times is less than the preset threshold, the central sensor corresponding to the third time difference with the smallest index difference is selected as the reference sensor, or the central sensor corresponding to the third time difference with the smallest difference from the preset average value is selected as the reference sensor.

8. The battery health determination system according to claim 6, characterized in that, The controller is also used for: Determine the pressure difference between all pressure values ​​monitored by each central sensor; For multiple pressure difference values ​​corresponding to all central sensors, if it is determined that all multiple pressure difference values ​​are less than or equal to a preset pressure threshold, the average value of the multiple pressure difference values ​​is determined. The difference between the mean value and the pressure difference values ​​traversed is calculated to obtain the sum of the squares of the difference. The reference sensor is determined based on the center sensor corresponding to the minimum value among the multiple sums of squares.

9. The battery health determination system according to claim 1, characterized in that, The controller is also used for: If it is determined that the reference sensor includes the other sensors, a second monitoring moment is determined when the other sensors detect the extreme value among all pressure values; The second extreme moment is determined based on the second monitoring moment; or, for all second monitoring moments corresponding to at least two other sensors, the second extreme moment is determined based on the average value of all the second monitoring moments.

10. The battery health determination system according to claim 9, characterized in that, The controller is also used for: In the case where the extreme value is a maximum value, the third maximum value time in the first extreme value time is determined, and the fourth maximum value time in the second extreme value time is determined; The fourth time difference between the third maximum time and the fourth maximum time is determined, and the health of the battery is determined based on the third comparison result of the fourth time difference and the third preset threshold.

11. The battery health determination system according to claim 9, characterized in that, The controller is also used for: In the case of the extreme value being a minimum value, the third minimum value time in the first extreme value time is determined, and the fourth minimum value time in the second extreme value time is determined; The fifth time difference between the third minimum time and the fourth minimum time is determined, and the health of the battery is determined based on the fourth comparison result of the fifth time difference and the fourth preset threshold.

12. The battery health determination system according to claim 1, characterized in that, The controller is also used for: If the first ratio of the target time difference to the preset charging time is greater than the fifth preset threshold, the battery health is determined to be abnormal. If the first ratio is determined to be less than or equal to the fifth preset threshold, the battery's health is determined to be normal.

13. The battery health determination system according to claim 1, characterized in that, The controller is also used for: If the second ratio of the target time difference to the preset discharge duration is greater than the sixth preset threshold, the health of the battery is determined to be abnormal. If the second ratio is determined to be less than or equal to the sixth preset threshold, the battery's health is determined to be normal.

14. The battery health determination system according to claim 13, characterized in that, The controller is also used for: If the absolute value of the target time difference is determined to be continuously increasing within the monitoring period, the health of the battery is determined to be abnormal. Alternatively, if the absolute value of the target time difference is determined to be continuously increasing within the monitoring period, and the rate of increase is greater than the growth rate threshold, the battery health is determined to be abnormal.

15. The battery health determination system according to claim 13, characterized in that, The controller is also used for: An abnormal pressure sensor is identified as having an anomaly, wherein the target comparison result of the abnormal pressure sensor is used to indicate that the target time difference is greater than the preset threshold. If the number of abnormal pressure sensors is greater than a preset number, the battery health is determined to be abnormal. If the number of abnormal pressure sensors accounts for a greater than a first preset ratio in the total number of all pressure sensors, the battery health is determined to be abnormal.

16. The battery health determination system according to claim 15, characterized in that, The controller is also used for: If the number of times the abnormal pressure sensor malfunctions exceeds a preset number, the battery's health is determined to be abnormal. If the number of times the abnormal pressure sensor malfunctions accounts for a greater than a second preset ratio in the total number of monitoring times of all pressure sensors, the health of the battery is determined to be abnormal.

17. A method for determining battery health, characterized in that, include: The pressure sensing data of the battery is determined based on the pressure values ​​monitored by the pressure sensors in the pressure sensor group. Each pressure sensor in the pressure sensor group is individually set at a grid point in the grid array to collect the pressure value within the grid point. For any pressure sensor in the pressure sensor group, determine the first extreme moment when the pressure sensor detects the extreme value among all pressure values; determine the second extreme moment when the reference sensor detects the extreme value among all pressure values; determine the target time difference between the first extreme moment and the second extreme moment; determine the target comparison result for each pressure sensor based on the target time difference and a preset threshold; traverse all pressure sensors and determine the battery health based on all target comparison results; wherein, the reference sensor includes other sensors in the pressure sensor group besides the current pressure sensor, and / or a reference sensor determined from the central region of the battery surface.

18. An electric vehicle, characterized in that, The system is provided with a battery health determination system as described in any one of claims 1 to 16.

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of claim 17.

20. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method of claim 17 through the computer program.

21. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 17.

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