Health state calculation method and device, medium and computer equipment

Abstract

The invention provides a power battery health state calculation method and device, a computer readable storage medium and computer equipment, and the method comprises the following steps: obtaining first data and second data of a target power battery, the first data comprises at least one of battery service data and vehicle operation parameters, and the second data comprises charging data of at least one charging process; determining a target cluster where the target power battery is located according to the first data; calculating an SOH local value of the target power battery according to the second data; and obtaining an SOH reference value corresponding to the target cluster, and correcting an SOH local value of the target power battery according to the SOH reference value. The SOH reference values of the same type of power batteries are used for correcting the SOH local values, obtained based on the data of the target power batteries, of the target power batteries, and high calculation accuracy can be achieved according to the health state calculation method.

Description

Technical Field

[0001] The present invention relates to the field of power batteries, and in particular to a health status calculation method, a health status calculation device, a computer-readable storage medium and a computer device for a power battery. Background Art

[0002] The state of health (SOH) is the main evaluation indicator of power batteries. It usually refers to the ratio of the capacity released when the power battery is discharged from a fully charged working state to a cut-off voltage at a certain rate to the corresponding nominal capacity. In order to ensure vehicle range and operating safety, accurately estimating the health status of the power battery has gradually become the focus.

[0003] The inventor knows a method for estimating the health state of a battery based on changes in the battery's internal resistance, wherein the battery's internal resistance is indirectly calculated based on detected voltage, current, temperature and other data, and the current health state value is calculated based on the corresponding relationship between the battery's internal resistance and the health state (or a characteristic curve of the two).

[0004] It is recognized that the change of the internal resistance value of the battery is not significant when the range of health status changes is not large, but when the battery state changes, such as severe aging, the change of the resistance value is relatively large, and this trend of the corresponding relationship will affect the calculation accuracy of the health status value. Furthermore, the corresponding relationship between the health status value and the internal resistance value used here is generally obtained through cycle testing experiments in the laboratory, which does not take into account the impact of different usage environments on the uniqueness of the above corresponding relationship of the power battery.

[0005] It should be noted that the content introduced here only provides background information related to the present disclosure and does not necessarily belong to the prior art. Summary of the invention

[0006] According to different aspects, the present invention aims to provide a health status calculation method with improved accuracy, and a health status calculation apparatus, a computer-readable storage medium, and a computer device capable of implementing such a method.

[0007] In addition, the present invention is also intended to solve or alleviate other technical problems existing in the prior art.

[0008] According to a first aspect of the present invention, a method for calculating the health status of a power battery is provided to solve the above problem. Specifically, the method comprises the following steps:

[0009] Acquire first data and second data of a target power battery, wherein the first data includes at least one of battery service data and vehicle operating parameters, and the second data includes charging data of at least one charging process;

[0010] Determining a target cluster where the target power battery is located according to the first data;

[0011] Calculating a local SOH value of the target power battery according to the second data;

[0012] The SOH reference value corresponding to the target cluster is obtained, and the SOH local value of the target power battery is corrected according to the SOH reference value.

[0013] In the health status calculation method proposed according to the first aspect of the present invention, the target cluster is one of the established target k clusters, and the establishment of the target k clusters includes:

[0014] Acquire the first data of m reference power batteries, take the first data of the target power battery and the first data of each reference power battery as a sample, and obtain (m+1) samples;

[0015] The (m+1) samples are clustered to obtain target k clusters.

[0016] In the health status calculation method proposed according to the first aspect of the present invention, clustering the (m+1) samples to obtain target k clusters includes:

[0017] Step A: Select k samples from the (m+1) samples, and use each of the k samples as the current cluster centroid, where k is an integer and 2≤k≤m+1;

[0018] Step B: clustering the (m+1) samples based on the distance from each of the current cluster centroids to generate the current k clusters;

[0019] Step C: Calculate the geometric center of the samples in each cluster, and update the current cluster centroid with the geometric center;

[0020] Step D: Repeat steps B to C until the current cluster centroid no longer changes, and obtain the current k clusters as the target k clusters.

[0021] In the health status calculation method proposed according to the first aspect of the present invention, the establishment of the target k clusters further includes:

[0022] Obtaining the total silhouette coefficient of the obtained target k clusters, and updating the current k value based on the total silhouette coefficient;

[0023] The (m+1) samples are clustered with the updated k value until the total silhouette coefficient reaches a preset threshold.

[0024] In the health status calculation method proposed according to the first aspect of the present invention, the SOH reference value corresponding to the target cluster is determined according to the following steps:

[0025] Acquiring second data of a reference power battery in the target cluster;

[0026] According to the second data, an average of the local SOH values ​​of the reference power battery is calculated as the SOH reference value.

[0027] In the health status calculation method proposed according to the first aspect of the present invention, the battery service data includes at least one of the power battery model, cumulative charging capacity, cumulative charging times, and offline time;

[0028] The vehicle operating parameters include at least one of driving condition parameters and driver characteristic parameters, wherein the driving condition parameters include at least one of cumulative mileage, cumulative number of trips, and power consumption per 100 kilometers, and the driver characteristic parameters include at least one of cumulative number of sudden accelerations, cumulative number of sudden decelerations, and cumulative number of sharp turns.

[0029] In the health status calculation method proposed according to the first aspect of the present invention, the charging data includes at least one of the charging current, the charging time, the charging start SOC and the charging end SOC during the charging process.

[0030] In the health status calculation method proposed according to the first aspect of the present invention, during the charging process, the deviation between the charge termination SOC and the charge start SOC is greater than 60%.

[0031] According to a second aspect of the present invention, a health status calculation device for executing the above method is also provided, comprising:

[0032] an acquisition module configured to acquire first data and second data of a target power battery, wherein the first data includes at least one of battery service data and vehicle operating parameters, and the second data includes charging data of at least one charging process;

[0033] a determination module configured to determine a target cluster where the target power battery is located according to the first data;

[0034] a calculation module configured to calculate a local SOH value of the target power battery according to the second data;

[0035] The correction module is configured to obtain a SOH reference value corresponding to the target cluster and correct the SOH local value according to the SOH reference value.

[0036] According to a third aspect of the present invention, a computer-readable storage medium is further provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the health status calculation method for a power battery described above is implemented.

[0037] According to a fourth aspect of the present invention, a computer device is also proposed, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-described method for calculating the health status of a power battery when executing the computer program.

[0038] By using the SOH reference value of the same type of power battery to correct the SOH local value of the target power battery obtained based on its own data, the health status calculation method according to the present invention can achieve higher calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other features of the present invention will become apparent with reference to the accompanying drawings, in which:

[0040] Figures 1 to 3 The main steps of an implementation of a method for calculating health status according to the present invention are shown;

[0041] Figure 4 The figure shows a structural block diagram of a health status calculation device according to the present invention. DETAILED DESCRIPTION

[0042] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific implementation modes and drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction to the technical solution of the present invention.

[0043] The directional terms such as up, down, left, right, front, back, front, back, top, bottom, etc. mentioned or may be mentioned in this specification are defined relative to the structures shown in the drawings. They are relative concepts and may change accordingly according to their different positions and different usage states. Therefore, these or other directional terms should not be interpreted as restrictive terms. In addition, the terms "first", "second", "third", etc. or similar expressions are only used for description and distinction purposes, and cannot be understood as indicating or implying the relative importance of the corresponding components.

[0044] In summary, the health status calculation method according to the present invention is mainly based on the following concept: the same type of power batteries should have the same or substantially the same SOH value (i.e., health status value) under the same or similar operating conditions. The SOH reference value SOH in a general sense determined by the reference power batteries in the same target cluster is ref To correct the SOH local value SOH obtained based on the charging process of the target power battery itself local , which can reduce the inaccuracy of health status calculation caused by data fluctuations or data bad points.

[0045] refer to Figures 1 to 3 The health status calculation method is described in detail, which shows the main steps or main sub-steps of the method. The health status calculation method mainly includes a data acquisition step, a target cluster determination step, a calculation step and a correction step, which will be described in detail one by one below.

[0046] It should be noted that the step names mentioned in this article (and those mentioned below) are only used to distinguish between the steps and facilitate the reference of the steps, and do not represent the order relationship between the steps. In the absence of obvious conflicts, unless otherwise specified, the steps can be executed in various orders or simultaneously.

[0047] In the data acquisition step, it is necessary to acquire the first data and the second data of the target power battery to be tested, wherein the first data includes battery service data, or vehicle operating parameters to which the target power battery belongs, or a combination of the two, and the second data includes charging data of at least one charging process. In the target cluster determination step, the target cluster where the target power battery is located is determined based on the acquired first data of the target power battery. The acquired second data is used to calculate the SOH local value SOH of the target power battery in the calculation step. local In the correction step, the SOH reference value SOH corresponding to the target cluster is obtained. ref , and based on it, correct the calculated SOH local value SOH of the target power battery local .

[0048] Here, the battery service data is used to characterize the characteristics of the service cycle of the power battery up to the current time point, especially the statistical characteristics. The battery service data can be called from the battery management system of the power battery. In an optional embodiment, the above-mentioned battery service data is selected from the following group, which includes the power battery model, the cumulative charging capacity up to the current time point, the cumulative number of charging times and the offline time, etc. Among them, the offline time refers to the time span of the power battery from the date of production to the current time point.

[0049] Regarding the vehicle operating parameters (wherein, the vehicle can be constructed in any form and is not limited to ordinary passenger cars), the following optional implementation methods are given, that is, it includes driving condition parameters and driver characteristic parameters, the driving condition parameters include at least one of the cumulative mileage, the cumulative number of trips, and the power consumption per 100 kilometers; the driver characteristic parameters include at least one of the cumulative number of sudden accelerations, the cumulative number of sudden decelerations, and the cumulative number of sharp turns. In addition, the driver characteristic parameters can also be generally expressed as a stable type or an aggressive type. Among them, the vehicle operating parameters can be called from the vehicle's on-board system. In this way, the two factors of vehicle driving conditions and driver driving habits are introduced in the health status calculation process. This is mainly due to the fact that changes in the discharge characteristics of the power battery caused by differences in vehicle operating conditions will affect the changing trend of the battery health status.

[0050] Optionally, the target cluster based on which the calculation process is performed is one of the established target k clusters, and the establishment of the target k clusters can be based on a cluster analysis process and includes the following steps:

[0051] Sample acquisition step: acquiring first data of m reference power batteries, taking the first data of the target power battery and the first data of each reference power battery as a sample respectively, to obtain (m+1) samples;

[0052] Cluster analysis step: cluster the (m+1) samples to obtain target k clusters.

[0053] Here, the preset number m is a positive integer. It should be understood by those skilled in the art that the preset number m is related to the accuracy of health status calculation and can be selected according to the number of cluster analysis samples. It is known that the larger the preset number m is, the better the cluster analysis result and calculation accuracy are. For example, m can be at least 100.

[0054] Regarding the sample acquisition step, an optional specific implementation method is given. The following data of the target power battery is used as the first data: cumulative charging capacity, cumulative charging times and offline time; the following data is collected from the vehicle system of the vehicle to which the target power battery belongs: cumulative mileage, cumulative driving times, power consumption per 100 kilometers, cumulative rapid acceleration times, cumulative rapid deceleration times, and cumulative rapid turning times. The collected data is preprocessed and integrated into a multidimensional array (here, a 9-dimensional array), recorded as X1.

[0055] Correspondingly, first data, especially first data of the same type as the target power battery, is collected for each reference power battery of the same type, and is respectively integrated into a multidimensional array. Each reference power battery corresponds to a multidimensional array, and the first data of the m reference power batteries collected can be recorded as: X2, X3, ..., X i ,…Xm+1 It should be noted here that the relevant parameters of the preset number m of reference power batteries can be obtained from the cloud database.

[0056] In addition, it should be noted that in the embodiment given here, the power battery model is not involved in the cluster analysis, that is, the first data does not include the power battery model, and the first data including the power battery model can be modified accordingly. For example, the power battery model can be involved in the cluster analysis calculation by designing the cluster analysis logic.

[0057] Based on the first data of the collected full power battery, cluster analysis is performed to obtain a target cluster with the same or similar operating conditions as the target power battery, wherein the full power battery includes the target power battery and the m reference power batteries. Here, the cluster analysis method can be but is not limited to K-means cluster analysis or DBSCAN cluster analysis. Next, the cluster analysis steps are described in detail based on the former.

[0058] Optionally, according to Figure 2 , perform the following sub-steps A to D in sequence to implement cluster analysis of all power batteries:

[0059] Step A: Select k samples from the (m+1) samples, and use each of the k samples as the current cluster centroid, where k is an integer and 2≤k≤m+1, for example, the k value can be set to 3. In other words, the k value can be determined randomly or according to a preset regulation, and k samples are randomly selected as the initial cluster centroids.

[0060] Step B: Clustering (m+1) samples based on the distance from each current cluster centroid to generate the current k clusters. Here, the full amount of power batteries are classified to the nearest current cluster centroid, such as the current starting cluster centroid. The distance can also be a Euclidean distance.

[0061] Step C: Calculate the geometric center of the samples in each cluster, and update the current cluster centroid with the geometric center. Specifically, for each of the k generated clusters, calculate the Euclidean geometric center of all samples assigned to the cluster, and use the obtained Euclidean geometric center to update the current cluster centroid.

[0062] Step D: Repeat steps B to C until the current cluster centroid no longer changes, and obtain the current k clusters as the target k clusters. Here, the judgment condition for the end of the cluster analysis step cycle is defined. At this time, the cluster where the target power battery is located is the above target cluster.

[0063] When determining the target cluster where the target power battery is located, the operating condition parameters of the main body to which the power battery belongs are involved in the cluster analysis process, thereby improving the accuracy of the cluster analysis and the subsequent health status calculation accuracy.

[0064] Optionally, the cluster analysis step further includes a k value updating step, specifically, including the following sub-steps:

[0065] Obtaining the total silhouette coefficient of the obtained target k clusters, and updating the current k value based on the total silhouette coefficient;

[0066] The (m+1) samples are clustered with the updated k value until the total silhouette coefficient reaches a preset threshold.

[0067] Specifically, in the above k value updating step, first, the average distance L between each sample of the full power battery and other samples in the same cluster is calculated. in ; Next, calculate the average distance L between each sample of the full power battery and other samples not in the same cluster out , then the silhouette coefficient s of each sample is s=(L out -L in ) / max(L out ,L in ). Then the total silhouette coefficient of the full power battery is the arithmetic sum of the silhouette coefficients of all samples, or it is also feasible to limit the total silhouette coefficient in other ways according to the preset rules. Here, an additional judgment condition for the end of the cycle of the cluster analysis process for finding the target cluster is actually given. By updating the k value, the accuracy and credibility of the cluster analysis process can be further improved.

[0068] Next, after finding the target cluster where the target power battery is located, in the correction step, the SOH reference value SOH determined by the reference power battery included in the target cluster is obtained. ref :

[0069] Acquiring second data of a reference power battery in the target cluster;

[0070] According to the second data, an average of the local SOH values ​​of the reference power battery is calculated as the SOH reference value.

[0071] It is understandable that the obtained SOH reference value can be used to correct the SOH local value SOH of any reference power battery in the target cluster in addition to being used to correct the target power battery. local , that is, data sharing can be achieved. Therefore, the possibility of simultaneously undertaking the SOH calculation task for multiple power batteries with less computing load is given here, which is undoubtedly beneficial to power battery operators.

[0072] Reference power battery SOH local value SOH local The local SOH value SOH of the target power battery in the acquisition and calculation steps local The acquisition of can be the same, and is described in detail here.

[0073] Used to calculate the SOH local value SOH of the target power battery and the reference power battery in the same target cluster local The second data includes charging data about at least one charging process, specifically, charging data of at least one charging process before the current time point. The charging data may involve current, voltage, temperature, instantaneous SOC (State of Charge) value, etc.

[0074] Specifically, for the second data, the current, charging time, charging start SOC and charging end SOC of at least one, preferably multiple charging processes are obtained from the battery management system of the target power battery. The charging data defined above for m reference power batteries are collected from the cloud database. It should be noted here that the acquisition of the second data of the reference power battery can be performed together with the acquisition of the first data in the cluster analysis step, and then only the second data of the reference power battery in the target cluster is called. Alternatively, it is also feasible to obtain the second data of the reference power battery contained therein after the target cluster is found. Alternatively, it is also feasible that the acquisition of the first data and the second data of all reference power batteries is performed together with the target power battery, and then called as needed. In addition, the acquisition and calling of the first data and the second data of the reference power battery can also be modified accordingly.

[0075] In an optional embodiment, the second data includes charging data such as the current, charging time, charging start SOC and charging end SOC in each charging process, and the battery capacity of this charging process is obtained by dividing it by the rated capacity of the battery to obtain the SOH local value SOH local .

[0076] Optionally, when calculating the SOH local value SOH of each reference power battery or target power battery in the target cluster, local When calculating the SOH calculation accuracy, the collected charging data can be cleaned and screened first to reduce the impact of charging data fluctuations on the accuracy of SOH calculation. For example, charging data in which the deviation between the charging end SOC and the charging start SOC is greater than 60% is considered valid.

[0077] For those skilled in the art, the SOH local value SOH of the target power battery or the reference power battery in the same target cluster can be calculated. localFor example, a feasible solution is also provided, in which the charging data in the second data includes the charging current and the charging voltage. First, the acquired charging voltage and charging current data are screened to obtain a complete charging process data curve; then, the battery capacity of the charging process is obtained by the time-ampere integral formula; finally, the obtained battery capacity is divided by the rated capacity of the battery to obtain the SOH local value SOH local .

[0078] Optionally, a specific example of how to correct the local SOH value of the target power battery is given here. In summary, according to the local SOH value SOH of the target power battery local SOH reference value SOH ref The deviation is used to calculate the SOH correction value SOH of the target power battery. corr Specifically, first, the deviation between the local SOH value of the target power battery and the SOH reference value |SOH local -SOH ref | and compare it with the preset deviation threshold Δ lim Make a comparison.

[0079] If |SOH local -SOH ref |<Δ lim , then the local SOH value SOH of the target power battery local SOH reference value SOH ref The average value of is recorded as the SOH correction value SOH of the target power battery corr , that is, SOH corr =(SOH local +SOH ref ) / 2.

[0080] In contrast, if |SOH local -SOH ref |≥Δ lim , then the SOH reference value SOH ref As the SOH correction value SOH of the target power battery corr , that is, SOH corr =SOH ref In this case, if the health status calculation device proposed according to the second aspect of the present invention can communicate with the vehicle alarm system, a warning of a power battery failure can be issued in time, and thus maintenance measures can be taken in time.

[0081] In summary, by using the SOH reference value of the same type (i.e. the same type of reference power battery under the same operating condition) to correct the SOH local value of the target power battery obtained based on its own data, the influence of bad points in its own data on the calculation accuracy can be eliminated and a higher calculation accuracy can be achieved. In one embodiment of the present invention, cluster analysis can simultaneously undertake the health status calculation tasks of multiple power batteries with less computing load. In another embodiment of the present invention, the influence of vehicle operating parameters and driver driving habits on the health status of the power battery is taken into account, thereby improving the accuracy of health status calculation. In another embodiment of the present invention, by screening the second data, the influence of charging data fluctuations on the accuracy of health status calculation can be reduced. In another embodiment of the present invention, by selectively correcting the SOH local value according to the deviation between the SOH local value of the target power battery and the SOH reference value, fault detection of the power battery can also be achieved in an additive manner.

[0082] In addition, according to the second aspect of the present invention, a health status calculation device 100 is provided for implementing the above method. Figure 4 As shown, the following modules are included:

[0083] An acquisition module 110, configured to acquire first data and second data of a target power battery, wherein the first data includes at least one of battery service data and vehicle operating parameters, and the second data includes charging data of at least one charging process;

[0084] A determination module 120, configured to determine a target cluster where the target power battery is located according to the first data;

[0085] a calculation module 130, configured to calculate a local SOH value of the target power battery according to the second data;

[0086] The correction module 140 is configured to obtain a SOH reference value corresponding to the target cluster and correct the SOH local value according to the SOH reference value.

[0087] The health status calculation device according to the present invention can be integrated into the battery management system of the vehicle, or optionally it can also be deployed in the cloud and can simultaneously take into account the health status calculation tasks of other vehicle power batteries. The health status calculation device according to the present invention can have the features and advantages described in the health status calculation method according to the present invention, and the description of the method can be referred to accordingly, and no further description is given for this.

[0088] It should be noted that the vehicle usage data involved in the disclosed embodiments are all information and data authorized by the user or fully authorized by all parties. The acquisition and collection of data involved in the disclosed embodiments are all performed after authorization by the user or the object or fully authorized by all parties.

[0089] The present invention also relates to a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements a method for calculating the state of health of a power battery according to one or more embodiments of the present invention. The computer-readable storage medium mentioned herein includes various types of computer storage media, which may be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the computer-readable storage medium may include RAM, ROM, EPROM, E2PROM, register, hard disk, removable disk, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other temporary or non-temporary medium that can be used to carry or store a desired program code unit in the form of an instruction or data structure and can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. With regard to the computer-readable storage medium according to the present invention, reference may be made to the explanation of the method for calculating the state of health or the device for calculating the state of health according to the present invention, which will not be repeated.

[0090] Finally, according to the fourth aspect of the present invention, a computer device is also proposed, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-described method for calculating the health status of a power battery when executing the computer program. The computer device can be configured as a controller or part of a battery management system of a mobile device such as a car or aircraft equipped with a power battery, or can also be configured as a server that communicates with the controller or battery management system. Regarding the computer device according to the present invention, reference can be made to the explanation of the health status calculation method or health status calculation device according to the present invention, which will not be repeated here.

[0091] It should be understood that all the above preferred embodiments are illustrative rather than restrictive, and various modifications or variations made by those skilled in the art to the specific embodiments described above under the concept of the present invention should be within the legal protection scope of the present invention.

Claims

1. A method for calculating the health status of a power battery, characterized in that: The steps include: Acquire first data and second data of a target power battery, wherein the first data includes at least one of battery service data and vehicle operating parameters, and the second data includes charging data of at least one charging process; Determining a target cluster where the target power battery is located according to the first data; Calculating a local SOH value of the target power battery according to the second data; The SOH reference value corresponding to the target cluster is obtained, and the SOH local value of the target power battery is corrected according to the SOH reference value.

2. The health status calculation method according to claim 1, characterized in that: The target cluster is one of the established target k clusters, and the establishment of the target k clusters includes: Acquire the first data of m reference power batteries, take the first data of the target power battery and the first data of each reference power battery as a sample, and obtain (m+1) samples; The (m+1) samples are clustered to obtain target k clusters.

3. The health status calculation method according to claim 2, characterized in that: The (m+1) samples are clustered to obtain target k clusters including: Step A: Select k samples from the (m+1) samples, and use each of the k samples as the current cluster centroid, where k is an integer and 2≤k≤m+1; Step B: clustering the (m+1) samples based on the distance from each of the current cluster centroids to generate the current k clusters; Step C: Calculate the geometric center of the samples in each cluster, and update the current cluster centroid with the geometric center; Step D: Repeat steps B to C until the current cluster centroid no longer changes, and obtain the current k clusters as the target k clusters.

4. The health status calculation method according to claim 2, characterized in that: The establishment of the target k clusters also includes: Obtaining the total silhouette coefficient of the obtained target k clusters, and updating the current k value based on the total silhouette coefficient; The (m+1) samples are clustered with the updated k value until the total silhouette coefficient reaches a preset threshold.

5. The health status calculation method according to claim 1, characterized in that: The SOH reference value corresponding to the target cluster is determined according to the following steps: Acquiring second data of a reference power battery in the target cluster; According to the second data, an average of the local SOH values ​​of the reference power battery is calculated as the SOH reference value.

6. The health status calculation method according to claim 1, characterized in that: The battery service data includes at least one of the power battery model, cumulative charging capacity, cumulative charging times, and offline time; The vehicle operating parameters include at least one of driving condition parameters and driver characteristic parameters, wherein the driving condition parameters include at least one of cumulative mileage, cumulative number of trips, and power consumption per 100 kilometers, and the driver characteristic parameters include at least one of cumulative number of sudden accelerations, cumulative number of sudden decelerations, and cumulative number of sharp turns.

7. The health status calculation method according to claim 1, characterized in that: The charging data includes at least one of a charging current, a charging time, a charging start SOC, and a charging end SOC during a charging process.

8. The health status calculation method according to claim 7, characterized in that: During the charging process, the deviation between the charge termination SOC and the charge start SOC is greater than 60%.

9. A device for calculating the health status of a power battery, characterized in that: The method can be used to perform the health status calculation method according to any one of claims 1 to 8 and comprises: an acquisition module configured to acquire first data and second data of a target power battery, wherein the first data includes at least one of battery service data and vehicle operating parameters, and the second data includes charging data of at least one charging process; a determination module configured to determine a target cluster where the target power battery is located according to the first data; a calculation module configured to calculate a local SOH value of the target power battery according to the second data; The correction module is configured to obtain a SOH reference value corresponding to the target cluster and correct the SOH local value according to the SOH reference value.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the health status calculation method for a power battery according to any one of claims 1 to 8 is implemented.

11. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the health status calculation method for a power battery according to any one of claims 1 to 8 is implemented.

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