Battery pack health state assessment method, device and equipment and storage medium

Abstract

The invention discloses a battery pack health state assessment method, device and equipment and a storage medium, and relates to the technical field of battery packs, and the method comprises the steps: obtaining the overall indexes of a target battery pack in different collection intervals of a preset time period, obtaining the monomer indexes of each monomer battery in the target battery pack in different acquisition intervals of a preset time period; a battery belief rule base is constructed, the battery belief rule base comprises a plurality of belief rules, and each belief rule is used for representing the mapping relation between the index range and belief distribution; screening a target belief rule corresponding to each collection interval from the battery belief rule base based on the overall index and the single index; and determining target belief distribution corresponding to the target belief rule, and determining a health state evaluation result of the target battery pack according to the target belief distribution. The overall health state of the battery pack can be comprehensively reflected.

Description

Technical Field

[0001] The present application relates to the field of battery pack technology, and in particular to a battery pack health status assessment method, device, equipment and storage medium. Background Art

[0002] The assessment of battery health status is of great significance in applications such as battery management systems (BMS), electric vehicles, and battery energy storage systems. However, traditional battery health status assessment methods generally have the problem of low assessment accuracy.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present application is to provide a battery pack health status assessment method, device, equipment and storage medium, aiming to solve the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods.

[0005] To achieve the above objectives, the present application proposes a battery pack health status assessment method, the battery pack health status assessment method comprising:

[0006] Acquire the overall index of the target battery group in different collection intervals during a preset time period, and acquire the single cell index of each single cell in the target battery group in different collection intervals during a preset time period;

[0007] Constructing a battery belief rule base, wherein the battery belief rule base includes a plurality of belief rules, each belief rule being used to characterize a mapping relationship between an indicator range and a belief distribution;

[0008] Based on the overall indicator and the single-cell indicator, a target belief rule corresponding to each collection interval is selected from the battery belief rule library;

[0009] A target belief distribution corresponding to the target belief rule is determined, and a health status assessment result of the target battery pack is determined according to the target belief distribution.

[0010] In one embodiment, the overall indicator includes overall voltage, overall current, overall temperature and bypass state, and the step of filtering out the target belief rule corresponding to each acquisition interval from the battery belief rule library based on the overall indicator and the single cell indicator includes:

[0011] Determine voltage inconsistency, current inconsistency and temperature inconsistency of the target battery group according to the single cell index of each single cell in the target battery group;

[0012] determining a comprehensive inconsistency based on the voltage inconsistency, the current inconsistency, and the temperature inconsistency;

[0013] According to the overall voltage, the overall current, the overall temperature, the bypass state and the comprehensive inconsistency, a target belief rule corresponding to each acquisition interval is screened out from the battery belief rule library.

[0014] In one embodiment, the single cell index includes single cell voltage, single cell current and single cell temperature; wherein the step of determining the voltage inconsistency, current inconsistency and temperature inconsistency of the target battery group according to the single cell index of each single cell in the target battery group includes:

[0015] Determine the voltage inconsistency of the target battery group according to the maximum cell voltage and the minimum cell voltage in the cell indicators;

[0016] Determine the current inconsistency of the target battery group according to the maximum single cell current and the minimum single cell current in the single cell indicators;

[0017] The temperature inconsistency of the target battery group is determined according to the highest cell temperature and the lowest cell temperature in the cell indicators.

[0018] In one embodiment, the step of determining a target belief distribution corresponding to the target belief rule, and determining a health status assessment result of the target battery pack according to the target belief distribution includes:

[0019] Using the overall voltage, the overall current, the overall temperature, the bypass state, and the comprehensive inconsistency as input data of the battery belief rule base;

[0020] Determining a rule activation weight of the target belief rule based on the input data, the rule weight, the rule matching degree, and the indicator importance weight;

[0021] Determining a target belief distribution corresponding to the target belief rule;

[0022] A health status assessment result of the target battery pack is determined according to the rule activation weight and the target belief distribution.

[0023] In one embodiment, the step of determining the health status assessment result of the target battery pack according to the rule activation weight and the target belief distribution includes:

[0024] Determining a healthy confidence, a slightly degraded confidence, and a severely degraded confidence in a final belief distribution according to the rule activation weights and the target belief distribution;

[0025] A health status assessment result of the target battery pack is determined according to the health confidence level, the slight degradation confidence level, and the severe degradation confidence level.

[0026] In one embodiment, the step of determining the health status assessment result of the target battery pack according to the health confidence level, the slight degradation confidence level, and the severe degradation confidence level includes:

[0027] Determine the health utility value, the slightly degraded utility value, and the severely degraded utility value;

[0028] Determining a health assessment value according to the health confidence and the health utility value;

[0029] determining a slight degradation assessment value according to the slight degradation confidence and the slight degradation utility value;

[0030] determining a severe degradation assessment value according to the severe degradation confidence and the severe degradation utility value;

[0031] Based on the health assessment value, the slight degradation assessment value, and the severe degradation assessment value, a health status assessment result of the target battery group is determined.

[0032] In one embodiment, the steps of obtaining the overall index of the target battery group in different collection intervals of a preset time period, and obtaining the single cell index of each single cell in the target battery group in different collection intervals of the preset time period include:

[0033] Obtaining from the satellite battery management system the initial overall index of the target battery group in different collection intervals during a preset time period, and obtaining the initial single cell index of each single cell in the target battery group in different collection intervals during a preset time period;

[0034] Cleaning the initial overall index and the initial single index, removing missing values, abnormal values ​​and noise values ​​in the initial overall index and the initial single index, and obtaining cleaned data;

[0035] The cleaned data is normalized to obtain overall indicators and individual indicators.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a battery pack health status assessment device, the battery pack health status assessment device comprising:

[0037] An acquisition module, used to acquire the overall index of the target battery group from the satellite battery management system, and to acquire the single cell index of each single cell in the target battery group;

[0038] A construction module, used to construct a battery belief rule base, wherein the battery belief rule base includes a plurality of belief rules, each belief rule is used to characterize a mapping relationship between an indicator range and a belief distribution;

[0039] A screening module, used to screen out target belief rules from the battery belief rule library based on the overall indicator and the single-cell indicator;

[0040] A determination module is used to determine a target belief distribution corresponding to the target belief rule, and determine a health status assessment result of the target battery pack according to the target belief distribution.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a battery pack health status assessment device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the battery pack health status assessment method as described above.

[0042] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the battery pack health status assessment method described above are implemented.

[0043] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the battery pack health status assessment method as described above are implemented.

[0044] One or more technical solutions proposed in this application have at least the following technical effects:

[0045] The battery pack health status assessment method, device, equipment and storage medium proposed in the present application obtain the overall index of the target battery pack in different collection intervals of a preset time period, and obtain the single cell index of each single cell in the target battery pack in different collection intervals of a preset time period; construct a battery belief rule library, wherein the battery belief rule library includes multiple belief rules, each belief rule is used to characterize the mapping relationship between the indicator range and the belief distribution; based on the overall index and the single cell index, the target belief rule corresponding to each collection interval is screened out from the battery belief rule library; the target belief distribution corresponding to the target belief rule is determined, and the health status assessment result of the target battery pack is determined according to the target belief distribution, which solves the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods. Compared with the prior art, the present application can fully consider the impact of the inconsistency of single cell indicators between single cells in the battery pack on the overall health status of the battery pack, and can also comprehensively consider the overall index performance of the battery pack, and more deeply understand the health status of the battery pack by tracking and analyzing the long-term operation data of the battery pack, which can effectively improve the assessment accuracy of the health status of the battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0048] Figure 1 A flowchart of the first embodiment of the method for evaluating the health status of a battery pack provided in the present application;

[0049] Figure 2 A schematic diagram of a belief rule base provided in Example 1 of the battery pack health status assessment method of the present application;

[0050] Figure 3 A flowchart of the second embodiment of the method for evaluating the health status of a battery pack provided in this application;

[0051] Figure 4 This is a schematic diagram of the module structure of a battery pack health status assessment device according to an embodiment of the present application;

[0052] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the battery pack health status assessment method in the embodiment of the present application.

[0053] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0055] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0056] The main solution of the embodiment of the present application is: obtaining the overall index of the target battery group in different collection intervals of a preset time period, and obtaining the single cell index of each single cell in the target battery group in different collection intervals of a preset time period; constructing a battery belief rule base, wherein the battery belief rule base includes multiple belief rules, each belief rule is used to characterize the mapping relationship between the index range and the belief distribution; based on the overall index and the single cell index, filtering out the target belief rule corresponding to each collection interval from the battery belief rule base; determining the target belief distribution corresponding to the target belief rule, and determining the health status assessment result of the target battery group according to the target belief distribution.

[0057] It can be seen from the above embodiments that the present application obtains the overall index of the target battery group in different collection intervals of a preset time period, and obtains the single cell index of each single cell in the target battery group in different collection intervals of a preset time period; constructs a battery belief rule library, wherein the battery belief rule library includes multiple belief rules, each belief rule is used to characterize the mapping relationship between the indicator range and the belief distribution; based on the overall index and the single cell index, the target belief rule corresponding to each collection interval is screened out from the battery belief rule library; the target belief distribution corresponding to the target belief rule is determined, and the health status assessment result of the target battery group is determined according to the target belief distribution, which solves the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods. Compared with the prior art, the present application can fully consider the impact of the inconsistency of single cell indicators between single cells in the battery pack on the overall health status of the battery pack, and can also comprehensively consider the overall index performance of the battery pack, and more deeply understand the health status of the battery pack by tracking and analyzing the long-term operation data of the battery pack, which can effectively improve the assessment accuracy of the health status of the battery pack.

[0058] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a battery pack health status assessment device, etc. The following takes the battery pack health status assessment as an example to illustrate this embodiment and the following embodiments.

[0059] Based on this, the present application embodiment provides a battery pack health status assessment method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the battery health status assessment method of the present application.

[0060] In this embodiment, the battery pack health status assessment method includes steps S10 to S40:

[0061] Step S10, obtaining the overall index of the target battery group in different collection intervals in a preset time period, and obtaining the single cell index of each single cell in the target battery group in different collection intervals in the preset time period;

[0062] It should be noted that the overall indicators and single cell indicators of the battery pack in different time periods can be obtained from the satellite battery management system (BMS). The overall indicators may include the overall voltage, overall current, overall temperature and bypass status, and the single cell indicators may include the single cell voltage, single cell current and single cell temperature.

[0063] It should be noted that the preset time period can be set manually, and different collection intervals refer to multiple collection intervals in the preset time period. For example, if the preset time period is 14:00-15:00, then the first collection interval may be 14:00-15:05, and the second collection interval may be 14:10-15:15.

[0064] In a feasible implementation, the steps of obtaining the overall index of the target battery group in different collection intervals of a preset time period, and obtaining the single cell index of each single cell in the target battery group in different collection intervals of the preset time period include: obtaining the initial overall index of the target battery group in different collection intervals of the preset time period from a satellite battery management system, and obtaining the initial single cell index of each single cell in the target battery group in different collection intervals of the preset time period; cleaning the initial overall index and the initial single cell index to remove missing values, abnormal values ​​and noise values ​​in the initial overall index and the initial single cell index to obtain cleaned data; normalizing the cleaned data to obtain the overall index and the single cell index.

[0065] It should be noted that after obtaining the overall indicators and single cell indicators of the target battery pack, these data need to be cleaned, such as eliminating outliers, missing values ​​and noise values ​​in the overall indicators and single cell indicators, and normalizing the data to ensure the consistency of the data size of different battery pack indicator parameters for subsequent processing.

[0066] Step S20, constructing a battery belief rule base, wherein the battery belief rule base includes a plurality of belief rules, each belief rule being used to characterize a mapping relationship between an indicator range and a belief distribution;

[0067] It should be noted that different belief rules correspond to different belief distributions. Each belief rule corresponds to a different overall voltage range, overall current range, overall temperature range, bypass state and comprehensive inconsistency. The belief rule corresponding to the battery pack can be determined based on the collected overall indicators and single cell indicators of the battery pack.

[0068] In the specific implementation, Figure 2 The belief rule library shown shows 10 belief rules, each of which corresponds to the voltage state, current state, temperature state, bypass state, comprehensive inconsistency and belief distribution of the battery pack as a whole. The belief distribution gives a "normal" belief value, a "slightly degraded" belief value, and a "severely degraded" belief value.

[0069] Step S30, based on the overall indicator and the single cell indicator, filter out the target belief rule corresponding to each collection interval from the battery belief rule library;

[0070] It should be noted that the preset time period includes multiple collection intervals. Multiple overall indicators and multiple single-cell indicators of the battery pack can be collected within the preset time period. Therefore, multiple target belief rules corresponding to the battery pack can be screened out within the preset time period.

[0071] It should be noted that the battery belief rule base can be established based on historical data or expert knowledge to define the relationship between different indicators and states, such as defining the association between indicators such as constant voltage, current and temperature and the battery health status.

[0072] In a feasible implementation manner, the overall indicators include overall voltage, overall current, overall temperature and bypass status, and the step of filtering out the target belief rules corresponding to each acquisition interval from the battery belief rule library based on the overall indicators and the single cell indicators includes: determining the voltage inconsistency, current inconsistency and temperature inconsistency of the target battery group according to the single cell indicators of each single cell in the target battery group; determining the comprehensive inconsistency based on the voltage inconsistency, the current inconsistency and the temperature inconsistency; filtering out the target belief rules corresponding to each acquisition interval from the battery belief rule library according to the overall voltage, the overall current, the overall temperature, the bypass status and the comprehensive inconsistency.

[0073] In a specific implementation, comprehensive inconsistency=(weight 1×voltage inconsistency+weight 2×temperature inconsistency+weight 3×temperature inconsistency) / total weight.

[0074] It should be noted that when the comprehensive inconsistency is greater than the first threshold, the comprehensive inconsistency can be evaluated as "large", when the comprehensive inconsistency is less than or equal to the first threshold and greater than or equal to the second threshold, the comprehensive inconsistency can be evaluated as "medium", and when the comprehensive inconsistency is less than the second threshold, the comprehensive inconsistency is evaluated as "large"; the bypass state includes bypass and no bypass, bypass means that there are single cells in the battery pack that are separated from the battery pack due to a fault problem, and the bypass state can be achieved by setting a specific switch or circuit in the battery pack to temporarily separate the single cell from the main circuit when the single cell fails, thereby protecting the performance and life of the battery pack.

[0075] In a feasible implementation manner, the single cell indicators include single cell voltage, single cell current and single cell temperature; wherein, the step of determining the voltage inconsistency, current inconsistency and temperature inconsistency of the target battery group according to the single cell indicators of each single cell in the target battery group includes: determining the voltage inconsistency of the target battery group according to the maximum single cell voltage and the minimum single cell voltage in the single cell indicators; determining the current inconsistency of the target battery group according to the maximum single cell current and the minimum single cell current in the single cell indicators; determining the temperature inconsistency of the target battery group according to the highest single cell temperature and the lowest single cell temperature in the single cell indicators.

[0076] It should be noted that the maximum single-cell voltage refers to the maximum voltage among the single-cell voltages of all single-cell batteries in the target battery group within the same acquisition interval; the minimum single-cell voltage refers to the minimum voltage among the single-cell voltages of all single-cell batteries in the target battery group within the same acquisition interval; the maximum single-cell current refers to the maximum current among the single-cell currents of all single-cell batteries in the target battery group within the same acquisition interval; the minimum single-cell current refers to the minimum current among the single-cell currents of all single-cell batteries in the target battery group within the same acquisition interval; the maximum single-cell temperature refers to the highest temperature among the single-cell temperatures of all single-cell batteries in the target battery group within the same acquisition interval; the minimum single-cell temperature refers to the lowest temperature among the single-cell temperatures of all single-cell batteries in the target battery group within the same acquisition interval.

[0077] In a specific implementation, voltage inconsistency is the difference between the maximum cell voltage and the minimum cell voltage; current inconsistency is the difference between the maximum cell current and the minimum cell current; temperature inconsistency is the difference between the highest cell temperature and the lowest cell temperature.

[0078] Step S40, determining a target belief distribution corresponding to the target belief rule, and determining a health status assessment result of the target battery pack according to the target belief distribution.

[0079] It should be noted that the real-time overall indicators and real-time single-cell indicators of the battery pack can be regularly collected from the satellite battery management system (BMS) to evaluate the health status of the battery pack and provide early warning prompts when the battery pack is abnormal or degraded.

[0080] It should be noted that since multiple overall indicators and multiple single-cell indicators of the target battery pack are collected in different collection intervals within a preset time period, multiple different belief rules can be activated according to the collected data of the target battery pack. Since different belief rules correspond to different belief distributions, it is necessary to weightedly average the belief distributions of all belief rules to obtain the final health status assessment result.

[0081] In the specific implementation, 1. Obtain the original data of battery pack operation: Obtain the original data of satellite lithium-ion battery pack from the satellite battery management system (BMS), including parameters such as voltage, temperature, capacity, discharge voltage and bypass status. Perform preliminary cleaning and preprocessing on these data, remove outliers and missing values, and normalize the data to ensure the consistency of data scale of different battery pack operation parameters for subsequent processing. 2. Use evidence reasoning (ER) to fuse multi-source inconsistency indicators: Extract inconsistency indicators from the preprocessed data, including voltage difference, temperature difference, capacity difference, etc. of single cells. In this process, use the evidence reasoning (ER) model to fuse multi-source inconsistency information, and generate a comprehensive inconsistency evaluation value by assigning weights to each indicator. The evidence reasoning method can handle the uncertainty brought by different data sources and generate comprehensive evaluation results in the form of belief degree distribution. 3. Construction of belief rule base (BRB): The belief rule base (BRB) is a reasoning system based on expert knowledge, and its rules are expressed in the form of "if-then". The core of BRB is to express the relevance of each rule to the input data through "belief degree". Belief degree refers to the confidence of the system in a certain evaluation result, that is, the possibility that the data conforms to a certain rule. For example, the input data may activate multiple rules at the same time, and the degree of activation of each rule is characterized by the belief degree. The higher the belief degree, the greater the influence of the rule on the final evaluation result. 4. The reasoning working principle of the belief rule base: After obtaining the inconsistency evaluation value of the battery pack, it is input into the belief rule base. BRB performs reasoning based on the performance indicators of the battery pack (such as capacity, discharge voltage, etc.) through the predefined rules in the rule base. Each rule calculates its activation weight based on the input data, and assigns weights through the belief degree to generate multiple belief distributions. Subsequently, the activated rules are fused using the evidence reasoning method, and the evaluation result is finally output. The reasoning ability of the belief rule base comes from its ability to process multiple uncertain information at the same time and realize automated reasoning through the weight allocation of rules. 5. Comprehensive evaluation of the health status of the battery pack: During the reasoning process of the belief rule base, multiple different rules will be activated according to the input data, and the final evaluation result is obtained by weighted calculation of the belief degrees of multiple activated rules. The core role of belief degree is to deal with uncertainty so that the evaluation results are distributed among multiple possible health states. Ultimately, the system integrates these belief degree distributions into a comprehensive health status assessment of the battery pack. 6. Model verification and optimization The model is verified using historical operating data of the satellite battery pack. A cross-validation method is used to evaluate the performance of the model on different satellite battery packs, especially its ability to accurately predict the inconsistency of battery cells and the overall performance of the battery pack. The optimal model parameters are determined through the validation set, and adjusted and optimized according to the operating conditions of different battery packs.

[0082] It should be noted that the comprehensive evaluation combines the inconsistency of battery cells with the overall performance of the battery pack, covering multiple important indicators of the battery pack (such as voltage difference, temperature difference, capacity, etc.), which can not only reflect the differences in battery cells, but also evaluate the overall health of the battery pack. Powerful uncertainty processing capability: The evidence reasoning (ER) method is adopted to integrate and evaluate multiple uncertainty factors through belief distribution to ensure that the evaluation results are still reliable when facing complex and uncertain data sources. This uncertainty processing capability enables the system to maintain a high evaluation accuracy when the operating environment of the battery pack changes greatly and the data sources are diverse. Automated reasoning and high adaptability: An automated health status assessment system is built through the belief rule base (BRB), combining expert knowledge with actual data to realize the automation of the assessment process. The BRB system dynamically activates different rules according to the input data and performs weighted fusion to ensure that the evaluation results are highly adaptable. Regardless of different types of battery packs or different working conditions, the system can flexibly adjust the rules to maintain accurate assessment. Real-time monitoring and early warning function: Supports real-time monitoring of the health status of the battery pack. Through the model deployed in the battery management system (BMS), it can collect battery data in real time and conduct dynamic evaluation. Once the health status of the battery pack is abnormal, the system will issue an early warning to help operators discover potential problems in advance and avoid premature failure or damage of the battery pack. This real-time evaluation and early warning function significantly improves the reliability and efficiency of battery management.

[0083] This embodiment obtains the overall index of the target battery group in different collection intervals of a preset time period, and obtains the single cell index of each single cell in the target battery group in different collection intervals of a preset time period; constructs a battery belief rule base, wherein the battery belief rule base includes multiple belief rules, each belief rule is used to characterize the mapping relationship between the index range and the belief distribution; based on the overall index and the single cell index, the target belief rule corresponding to each collection interval is screened out from the battery belief rule base; the target belief distribution corresponding to the target belief rule is determined, and the health status assessment result of the target battery group is determined according to the target belief distribution, which solves the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods. Compared with the prior art, the present application can fully consider the impact of the inconsistency of single cell indexes between single cells in the battery pack on the overall health status of the battery pack, and can also comprehensively consider the overall index performance of the battery pack, and more deeply understand the health status of the battery pack by tracking and analyzing the long-term operation data of the battery pack, which can effectively improve the assessment accuracy of the health status of the battery pack.

[0084] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , step S40 also includes steps S401 to S404:

[0085] Step S401, taking the overall voltage, the overall current, the overall temperature, the bypass state and the comprehensive inconsistency as input data of the battery belief rule base;

[0086] Step S402, determining the rule activation weight of the target belief rule based on the input data, the rule weight, the rule matching degree and the indicator importance weight;

[0087] It should be noted that the specific calculation formula for the rule activation weight is as follows:

[0088]

[0089] In the formula, θ k is the weight of the belief rule (usually determined by historical data or set manually); α k is the matching degree between the input data and the rule premise k (i.e. similarity, rule matching degree); δ k is the importance weight of each indicator (i.e., indicator importance weight), M refers to the number of evidences involved in reasoning (i.e., input data), and L refers to the total number of rules.

[0090] It should be noted that when the input data is close to the rule premise, the similarity is higher and the activation weight of the rule will also be higher.

[0091] Step S403, determining the target belief distribution corresponding to the target belief rule;

[0092] Step S404: determining a health status assessment result of the target battery pack according to the rule activation weight and the target belief distribution.

[0093] In a feasible implementation manner, the step of determining the health status assessment result of the target battery pack according to the rule activation weights and the target belief distribution includes: determining the health confidence, slight degradation confidence and severe degradation confidence in the final belief distribution according to the rule activation weights and the target belief distribution; determining the health status assessment result of the target battery pack according to the health confidence, the slight degradation confidence and the severe degradation confidence.

[0094] It should be noted that when multiple belief rules are activated, the belief distributions of these belief rules need to be merged to obtain the final health status belief distribution. The specific fusion method is as follows:

[0095]

[0096] In the formula, β j is the final belief distribution of health state j (e.g., “normal,” “slightly degraded,” “severely degraded”), such as the confidence of health, the confidence of slight degradation, and the confidence of severe degradation; ω i is the rule activation weight of rule i; β i,j is the initial belief distribution of rule i for state j; K is the normalization coefficient used to ensure that the sum of belief values ​​is 1.

[0097] In a feasible implementation manner, the step of determining the health status assessment result of the target battery pack according to the health confidence, the slight degradation confidence and the severe degradation confidence includes: determining a health utility value, a slight degradation utility value and a severe degradation utility value; determining a health assessment value according to the health confidence and the health utility value; determining a slight degradation assessment value according to the slight degradation confidence and the slight degradation utility value; determining a severe degradation assessment value according to the severe degradation confidence and the severe degradation utility value;

[0098] Based on the health assessment value, the slight degradation assessment value, and the severe degradation assessment value, a health status assessment result of the target battery group is determined.

[0099] In the specific implementation, the specific calculation of the health status assessment result of the target battery pack is as follows:

[0100]

[0101] Where N is the number of health states (in your case, there are 3 health states: normal, slightly degraded, and severely degraded, so N = 3); μ j is the utility value of each health state, for example, for the “normal state, the health utility value μ 正常 =1; for the “slightly degraded” state, the slightly degraded utility value

[0102] μ 轻微退化 =0.7; for the “severely degraded” state, μ 严重退化 =0.4; β j is the confidence of each health state, which is calculated by the fusion of rules, for example: β 正常 is the health confidence, β 轻微退化 is the slightly degenerate confidence, β 严重退化 is a severely degraded confidence.

[0103] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the battery pack health status assessment method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0104] This application also provides a battery pack health status assessment device, please refer to Figure 4 , the battery pack health status assessment device comprises:

[0105] An acquisition module 10 is used to acquire the overall index of the target battery group in different acquisition intervals in a preset time period, and to acquire the single cell index of each single cell in the target battery group in different acquisition intervals in a preset time period;

[0106] A construction module 20 is used to construct a battery belief rule base, wherein the battery belief rule base includes a plurality of belief rules, each belief rule is used to characterize a mapping relationship between an indicator range and a belief distribution;

[0107] A screening module 30, configured to screen out a target belief rule corresponding to each collection interval from the battery belief rule library based on the overall indicator and the single-cell indicator;

[0108] The determination module 40 is used to determine the target belief distribution corresponding to the target belief rule, and determine the health status assessment result of the target battery pack according to the target belief distribution.

[0109] The battery pack health status assessment device provided by the present application adopts the battery pack health status assessment method in the above embodiment, which can solve the technical problem of low assessment accuracy commonly existing in the traditional battery pack health status assessment method. Compared with the prior art, the beneficial effects of the battery pack health status assessment device provided by the present application are the same as the beneficial effects of the battery pack health status assessment method provided by the above embodiment, and the other technical features in the battery pack health status assessment device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0110] The present application provides a battery pack health status assessment device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the battery pack health status assessment method in the above-mentioned embodiment one.

[0111] Reference below Figure 5, which shows a schematic diagram of the structure of a battery pack health status assessment device suitable for implementing an embodiment of the present application. The battery pack health status assessment device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The battery pack health status assessment device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0112] like Figure 5 As shown, the battery pack health status assessment device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the battery pack health status assessment device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the battery pack health status assessment device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a battery pack health status assessment device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0113] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0114] The battery pack health status assessment device provided by the present application adopts the battery pack health status assessment method in the above embodiment, which can solve the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods. Compared with the prior art, the beneficial effects of the battery pack health status assessment device provided by the present application are the same as the beneficial effects of the battery pack health status assessment method provided by the above embodiment, and the other technical features in the battery pack health status assessment device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0115] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0116] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0117] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the battery pack health status assessment method in the above-mentioned embodiment.

[0118] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0119] The computer-readable storage medium may be included in the battery pack health status assessment device; or may exist independently without being assembled into the battery pack health status assessment device.

[0120] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the battery pack health status assessment device, the battery pack health status assessment device: (exclusive content).

[0121] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0122] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0124] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned battery pack health status assessment method, and can solve the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the battery pack health status assessment method provided in the above-mentioned embodiment, and will not be repeated here.

[0125] The present application also provides a computer program product, including a computer program, which implements the steps of the battery pack health status assessment method as described above when the computer program is executed by a processor.

[0126] The computer program product provided by the present application can solve the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the battery pack health status assessment method provided by the above embodiment, which will not be repeated here.

[0127] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

[0128] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for evaluating the health status of a battery pack, characterized in that: The method comprises: Acquire the overall index of the target battery group in different collection intervals during a preset time period, and acquire the single cell index of each single cell in the target battery group in different collection intervals during a preset time period; Constructing a battery belief rule base, wherein the battery belief rule base includes a plurality of belief rules, each belief rule being used to characterize a mapping relationship between an indicator range and a belief distribution; Based on the overall indicator and the single-cell indicator, a target belief rule corresponding to each collection interval is selected from the battery belief rule library; A target belief distribution corresponding to the target belief rule is determined, and a health status assessment result of the target battery pack is determined according to the target belief distribution.

2. The method according to claim 1, characterized in that The overall indicators include overall voltage, overall current, overall temperature and bypass status. The step of selecting target belief rules corresponding to each acquisition interval from the battery belief rule library based on the overall indicators and the single cell indicators includes: Determine voltage inconsistency, current inconsistency and temperature inconsistency of the target battery group according to the single cell index of each single cell in the target battery group; determining a comprehensive inconsistency based on the voltage inconsistency, the current inconsistency, and the temperature inconsistency; According to the overall voltage, the overall current, the overall temperature, the bypass state and the comprehensive inconsistency, a target belief rule corresponding to each acquisition interval is screened out from the battery belief rule library.

3. The method according to claim 2, characterized in that The single cell index includes single cell voltage, single cell current and single cell temperature; wherein the step of determining the voltage inconsistency, current inconsistency and temperature inconsistency of the target battery group according to the single cell index of each single cell in the target battery group includes: Determine the voltage inconsistency of the target battery group according to the maximum cell voltage and the minimum cell voltage in the cell indicators; Determine the current inconsistency of the target battery group according to the maximum single cell current and the minimum single cell current in the single cell indicators; The temperature inconsistency of the target battery group is determined according to the highest cell temperature and the lowest cell temperature in the cell indicators.

4. The method according to claim 2, characterized in that The step of determining a target belief distribution corresponding to the target belief rule, and determining a health status assessment result of the target battery pack according to the target belief distribution includes: Using the overall voltage, the overall current, the overall temperature, the bypass state, and the comprehensive inconsistency as input data of the battery belief rule base; Determining a rule activation weight of the target belief rule based on the input data, the rule weight, the rule matching degree, and the indicator importance weight; Determining a target belief distribution corresponding to the target belief rule; A health status assessment result of the target battery pack is determined according to the rule activation weight and the target belief distribution.

5. The method according to claim 4, characterized in that The step of determining the health status assessment result of the target battery pack according to the rule activation weight and the target belief distribution includes: Determining healthy confidence, slightly degraded confidence, and severely degraded confidence in a final belief distribution according to the rule activation weights and the target belief distribution; A health status assessment result of the target battery pack is determined according to the health confidence level, the slight degradation confidence level, and the severe degradation confidence level.

6. The method according to claim 5, characterized in that The step of determining the health status assessment result of the target battery pack according to the health confidence level, the slight degradation confidence level, and the severe degradation confidence level comprises: Determine the health utility value, the slightly degraded utility value, and the severely degraded utility value; Determining a health assessment value according to the health confidence and the health utility value; determining a slight degradation assessment value according to the slight degradation confidence and the slight degradation utility value; determining a severe degradation assessment value according to the severe degradation confidence and the severe degradation utility value; Based on the health assessment value, the slight degradation assessment value, and the severe degradation assessment value, a health status assessment result of the target battery group is determined.

7. The method according to claim 1, characterized in that The steps of obtaining the overall index of the target battery group in different collection intervals of the preset time period, and obtaining the single cell index of each single cell in the target battery group in different collection intervals of the preset time period include: Obtaining from the satellite battery management system the initial overall index of the target battery group in different collection intervals during a preset time period, and obtaining the initial single cell index of each single cell in the target battery group in different collection intervals during a preset time period; Cleaning the initial overall index and the initial single index, removing missing values, abnormal values ​​and noise values ​​in the initial overall index and the initial single index, and obtaining cleaned data; The cleaned data is normalized to obtain overall indicators and individual indicators.

8. A battery pack health status assessment device, characterized in that: The device comprises: An acquisition module, used to acquire the overall index of the target battery group in different acquisition intervals in a preset time period, and to acquire the single cell index of each single cell in the target battery group in different acquisition intervals in a preset time period; A construction module, used to construct a battery belief rule base, wherein the battery belief rule base includes a plurality of belief rules, each belief rule is used to characterize a mapping relationship between an indicator range and a belief distribution; A screening module, used to screen out a target belief rule corresponding to each collection interval from the battery belief rule library based on the overall indicator and the single-cell indicator; A determination module is used to determine a target belief distribution corresponding to the target belief rule, and determine a health status assessment result of the target battery pack according to the target belief distribution.

9. A battery pack health status assessment device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the battery health status assessment method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the battery pack health status assessment method according to any one of claims 1 to 7 are implemented.

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