Method, apparatus and device for assessing state of health of battery pack, and storage medium

Abstract

Disclosed in the present application are a method, apparatus and device for assessing the state of health of a battery pack, and a storage medium. The method comprises: acquiring pack-level indicators of a target battery pack within different collection intervals of a preset time period, and acquiring cell-level indicators of battery cells in the target battery pack within the different collection intervals of the preset time period; constructing a battery belief rule base; selecting from the battery belief rule base a target belief rule corresponding to each collection interval; and determining target belief distributions corresponding to the target belief rules, and determining, on the basis of the target belief distributions, a state-of-health assessment result of the target battery pack.

Description

Battery pack health status assessment methods, apparatus, equipment and storage media

[0001] Related applications

[0002] This application claims priority to Chinese Patent Application No. 202411831119.3, filed with the Chinese Patent Office on December 12, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of battery pack technology, and in particular to battery pack health status assessment methods, apparatus, equipment and storage media. Background Technology

[0004] Assessing the health status of battery packs is crucial in applications such as battery management systems (BMS), electric vehicles, and battery energy storage systems. However, traditional battery pack health status assessment methods generally suffer from low accuracy.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide a method, apparatus, device and storage medium for assessing the health status of a battery pack, which aims to solve the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods.

[0007] To achieve the above objectives, this application proposes a battery pack health status assessment method, which includes:

[0008] The overall indicators of the target battery pack are obtained within different collection intervals of a preset time period, and the individual indicators of each cell in the target battery pack are obtained within different collection intervals of a preset time period.

[0009] A battery belief rule base is constructed, 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;

[0010] Based on the overall index and the individual index, target belief rules corresponding to each collection interval are selected from the battery belief rule base;

[0011] Determine the target belief distribution corresponding to the target belief rule, and determine the health status assessment result of the target battery pack based on the target belief distribution.

[0012] In one embodiment, the overall indicators include overall voltage, overall current, overall temperature, and bypass status. The step of selecting the target belief rule corresponding to each collection interval from the battery belief rule base based on the overall indicators and the individual cell indicators includes:

[0013] The voltage inconsistency, current inconsistency, and temperature inconsistency of the target battery pack are determined based on the individual cell specifications of each cell in the target battery pack.

[0014] Based on the voltage inconsistency, the current inconsistency, and the temperature inconsistency, a comprehensive inconsistency is determined.

[0015] Based on the overall voltage, overall current, overall temperature, bypass status, and overall inconsistency, target belief rules corresponding to each acquisition interval are selected from the battery belief rule library.

[0016] In one embodiment, the individual cell indicators include individual cell voltage, individual cell current, and individual cell temperature; wherein, the step of determining the voltage inconsistency, current inconsistency, and temperature inconsistency of the target battery pack based on the individual cell indicators of each cell in the target battery pack includes:

[0017] The voltage inconsistency of the target battery pack is determined based on the maximum and minimum single-cell voltages in the single-cell indicators.

[0018] The current inconsistency of the target battery pack is determined based on the maximum and minimum single-cell currents in the single-cell indicators.

[0019] The temperature inconsistency of the target battery pack is determined based on the highest and lowest individual cell temperatures in the individual cell indicators.

[0020] In one embodiment, the step of determining the target belief distribution corresponding to the target belief rule and determining the health status assessment result of the target battery pack based on the target belief distribution includes:

[0021] The overall voltage, overall current, overall temperature, bypass state, and overall inconsistency are used as input data for the battery belief rule base.

[0022] Based on the input data, rule weights, rule matching degree, and indicator importance weights, the rule activation weights of the target belief rules are determined.

[0023] Determine the distribution of target beliefs corresponding to the aforementioned target belief rules;

[0024] The health status assessment result of the target battery pack is determined based on the rule activation weights and the target belief distribution.

[0025] In one embodiment, the step of determining the health status assessment result of the target battery pack based on the rule activation weights and the target belief distribution includes:

[0026] Based on the activation weights of the rules and the target belief distribution, determine the health confidence, slight degradation confidence, and severe degradation confidence in the final belief distribution;

[0027] The health status assessment result of the target battery pack is determined based on the health confidence level, the slight degradation confidence level, and the severe degradation confidence level.

[0028] In one embodiment, the step of determining the health status assessment result of the target battery pack based on the health confidence level, the slight degradation confidence level, and the severe degradation confidence level includes:

[0029] Determine the health utility value, mild deterioration utility value, and severe deterioration utility value;

[0030] The health assessment value is determined based on the health confidence level and the health utility value;

[0031] The assessment value for minor degradation is determined based on the confidence level of minor degradation and the utility value of minor degradation;

[0032] The severe degradation assessment value is determined based on the severe degradation confidence level and the severe degradation utility value;

[0033] Based on the health assessment value, the slight degradation assessment value, and the severe degradation assessment value, the health status assessment result of the target battery pack is determined.

[0034] In one embodiment, the steps of obtaining the overall indicators of the target battery pack within different collection intervals of a preset time period, and obtaining the individual indicators of each cell in the target battery pack within different collection intervals of the preset time period, include:

[0035] The initial overall index of the target battery pack in different collection intervals within a preset time period is obtained from the satellite battery management system, and the initial individual index of each cell in the target battery pack in different collection intervals within a preset time period is obtained.

[0036] The initial overall index and the initial individual index are cleaned to remove missing values, outliers and noise values, resulting in cleaned data.

[0037] The cleaned data is normalized to obtain overall indicators and individual indicators.

[0038] Furthermore, to achieve the above objectives, this application also proposes a battery pack health status assessment device, which includes:

[0039] The acquisition module is used to acquire the overall indicators of the target battery pack from the satellite battery management system, and to acquire the individual indicators of each cell in the target battery pack.

[0040] A construction module is used to construct 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;

[0041] The filtering module is used to filter target belief rules from the battery belief rule base based on the overall index and the individual index.

[0042] The determination module is used to determine the target belief distribution corresponding to the target belief rule, and to determine the health status assessment result of the target battery pack based on the target belief distribution.

[0043] In addition, to achieve the above objectives, this application also proposes a battery pack health status assessment device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the battery pack health status assessment method as described above.

[0044] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the battery pack health status assessment method described above.

[0045] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the battery pack health status assessment method described above.

[0046] One or more technical solutions proposed in this application have at least the following technical effects:

[0047] The battery pack health status assessment method, apparatus, device, and storage medium proposed in this application acquire the overall indicators of the target battery pack within different collection intervals over a preset time period, and acquire the individual indicators of each cell in the target battery pack within different collection intervals over a preset time period; construct a battery belief rule library, wherein the battery belief rule library includes multiple belief rules, each belief rule representing the mapping relationship between indicator range and belief distribution; based on the overall indicators and the individual indicators, target belief rules corresponding to each collection interval are selected 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. This solves the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods. Compared with the prior art, this application can fully consider the impact of the inconsistency of individual indicators between cells within the battery pack on the overall health status of the battery pack, and can also comprehensively consider the overall indicator performance of the battery pack. By tracking and analyzing the long-term operating data of the battery pack, a deeper understanding of the battery pack health status can be achieved, thus effectively improving the accuracy of battery pack health status assessment. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 is a flowchart of the battery pack health status assessment method provided in Embodiment 1 of this application;

[0051] Figure 2 is a schematic diagram of the belief rule base provided in Embodiment 1 of the battery pack health status assessment method of this application;

[0052] Figure 3 is a flowchart of the battery pack health status assessment method provided in Embodiment 2 of this application;

[0053] Figure 4 is a schematic diagram of the module structure of the battery pack health status assessment device according to an embodiment of this application;

[0054] Figure 5 is a schematic diagram of the hardware operating environment involved in the battery pack health status assessment method in this application embodiment.

[0055] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0057] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0058] The main solution of this application embodiment is as follows: Obtain the overall indicators of the target battery pack within different collection intervals of a preset time period, and obtain the individual indicators of each cell in the target battery pack within different collection intervals of the preset time period; construct a battery belief rule base, wherein the battery belief rule base includes multiple belief rules, each belief rule being used to characterize the mapping relationship between the indicator range and the belief distribution; based on the overall indicators and the individual indicators, select the target belief rule corresponding to each collection interval from the battery belief rule base; determine the target belief distribution corresponding to the target belief rule, and determine the health status assessment result of the target battery pack based on the target belief distribution.

[0059] As can be seen from the above embodiments, this application obtains the overall indicators of the target battery pack within different collection intervals of a preset time period, and obtains the individual indicators of each cell in the target battery pack within 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 being used to characterize the mapping relationship between the indicator range and the belief distribution; based on the overall indicators and the individual indicators, it selects the target belief rule corresponding to each collection interval from the battery belief rule base; determines the target belief distribution corresponding to the target belief rule, and determines the health status assessment result of the target battery pack based on the target belief distribution. This solves the technical problem of low assessment accuracy that is common in traditional battery pack health status assessment methods. Compared with the prior art, this application can fully consider the impact of the inconsistency of individual indicators between cells within the battery pack on the overall health status of the battery pack, and can also comprehensively consider the overall indicator performance of the battery pack. By tracking and analyzing the long-term operating data of the battery pack, it can gain a deeper understanding of the battery pack health status, thus effectively improving the accuracy of battery pack health status assessment.

[0060] The executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or battery pack health status assessment device capable of performing the above functions. The following description uses battery pack health status assessment as an example to illustrate this embodiment and the subsequent embodiments.

[0061] Based on this, this application provides a method for assessing the health status of a battery pack. Referring to Figure 1, which is a flowchart of the first embodiment of the method for assessing the health status of a battery pack, this application provides a method for assessing the health status of a battery pack.

[0062] In this embodiment, the battery pack health status assessment method includes steps S10 to S40:

[0063] Step S10: Obtain the overall index of the target battery pack in different collection intervals within a preset time period, and obtain the individual index of each cell in the target battery pack in different collection intervals within a preset time period.

[0064] The overall and individual cell indicators of the battery pack can be obtained from the satellite battery management system (BMS) at different time periods. The overall indicators can include overall voltage, overall current, overall temperature and bypass status, while the individual cell indicators can include individual cell voltage, individual cell current and individual cell temperature.

[0065] The preset time period can be set manually. Different collection intervals refer to multiple collection intervals within the preset time period. For example, if the preset time period is 14:00-15:00, then the first collection interval can be 14:00-15:05, and the second collection interval can be 14:10-15:15.

[0066] In one embodiment, the steps of obtaining the overall indicators of the target battery pack within different acquisition intervals of a preset time period, and obtaining the individual indicators of each cell in the target battery pack within different acquisition intervals of a preset time period, include: obtaining the initial overall indicators of the target battery pack within different acquisition intervals of a preset time period from the satellite battery management system, and obtaining the initial individual indicators of each cell in the target battery pack within different acquisition intervals of a preset time period; cleaning the initial overall indicators and the initial individual indicators to remove missing values, outliers, and noise values, obtaining cleaned data; and normalizing the cleaned data to obtain the overall indicators and individual indicators.

[0067] After obtaining the overall and individual battery pack metrics of the target battery pack, the data needs to be cleaned, such as removing outliers, missing values, and noise values ​​from the overall and individual metrics, and normalizing the data to ensure the consistency of data size for different battery pack metrics parameters for subsequent processing.

[0068] Step S20: Construct a battery belief rule base, wherein the battery belief rule base includes multiple belief rules, each belief rule being used to characterize the mapping relationship between the index range and the belief distribution;

[0069] 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 overall inconsistency. The belief rule corresponding to the battery pack can be determined based on the overall indicators of the battery pack and the indicators of each individual cell.

[0070] In the specific implementation, as shown in Figure 2, the belief rule library displays 10 belief rules. Each belief rule corresponds to the overall voltage state, current state, temperature state, bypass state, overall inconsistency, and belief distribution of the battery pack. The belief distribution provides the belief value for "normal", the belief value for "slight degradation", and the belief value for "severe degradation".

[0071] Step S30: Based on the overall index and the individual index, select the target belief rule corresponding to each collection interval from the battery belief rule library;

[0072] The preset time period includes multiple collection intervals. Within the preset time period, multiple overall indicators and multiple individual indicators of the battery pack can be collected. Therefore, multiple target belief rules corresponding to the battery pack can be filtered out within the preset time period.

[0073] A battery belief rule base can be built based on historical data or expert knowledge to define the relationships between different indicators and states, such as defining the correlation between indicators such as voltage, current and temperature and battery health status.

[0074] In one embodiment, the overall indicators include overall voltage, overall current, overall temperature, and bypass status. The step of selecting target belief rules corresponding to each sampling interval from the battery belief rule base based on the overall indicators and the individual cell indicators includes: determining voltage inconsistency, current inconsistency, and temperature inconsistency of the target battery pack based on the individual cell indicators of each cell in the target battery pack; determining comprehensive inconsistency based on the voltage inconsistency, the current inconsistency, and the temperature inconsistency; and selecting target belief rules corresponding to each sampling interval from the battery belief rule base based on the overall voltage, the overall current, the overall temperature, the bypass status, and the comprehensive inconsistency.

[0075] In the specific implementation, the overall inconsistency = (weight 1 × voltage inconsistency + weight 2 × temperature inconsistency + weight 3 × temperature inconsistency) / total weight.

[0076] When the overall inconsistency exceeds the first threshold, it can be assessed as "large"; when the overall inconsistency is less than or equal to the first threshold and greater than or equal to the second threshold, it can be assessed as "medium"; when the overall inconsistency is less than the second threshold, it can be assessed as "large". Bypass status includes both with and without bypass. With bypass refers to the presence of individual cells separated from the battery pack due to faults. Bypass status can be achieved by setting specific switches or circuits within the battery pack to temporarily disconnect individual cells from the main circuit when a fault occurs, thereby protecting the performance and lifespan of the battery pack.

[0077] In one embodiment, the individual cell indicators include individual cell voltage, individual cell current, and individual cell temperature; wherein, the step of determining the voltage inconsistency, current inconsistency, and temperature inconsistency of the target battery pack based on the individual cell indicators of each individual cell in the target battery pack includes: determining the voltage inconsistency of the target battery pack based on the maximum and minimum individual cell voltages in the individual cell indicators; determining the current inconsistency of the target battery pack based on the maximum and minimum individual cell currents in the individual cell indicators; and determining the temperature inconsistency of the target battery pack based on the highest and lowest individual cell temperatures in the individual cell indicators.

[0078] The maximum single-cell voltage refers to the highest voltage among all single-cell voltages in the target battery pack within the same acquisition interval; the minimum single-cell voltage refers to the lowest voltage among all single-cell voltages in the target battery pack within the same acquisition interval; the maximum single-cell current refers to the highest current among all single-cell currents in the target battery pack within the same acquisition interval; the minimum single-cell current refers to the lowest current among all single-cell currents in the target battery pack within the same acquisition interval; the highest single-cell temperature refers to the highest temperature among all single-cell temperatures in the target battery pack within the same acquisition interval; and the lowest single-cell temperature refers to the lowest temperature among all single-cell temperatures in the target battery pack within the same acquisition interval.

[0079] In specific implementations, voltage inconsistency is the difference between the maximum and minimum single-cell voltage; current inconsistency is the difference between the maximum and minimum single-cell current; and temperature inconsistency is the difference between the highest and lowest single-cell temperature.

[0080] Step S40: Determine the target belief distribution corresponding to the target belief rule, and determine the health status assessment result of the target battery pack based on the target belief distribution.

[0081] The health status of the battery pack can be assessed by periodically collecting real-time overall indicators and real-time individual cell indicators from the satellite battery management system (BMS), and early warnings can be provided when abnormalities or degradation occur in the battery pack.

[0082] Since multiple overall indicators and multiple individual indicators of the target battery pack are collected within different collection intervals of a preset time period, multiple different belief rules can be activated based on the collected data of the target battery pack. Since different belief rules correspond to different belief distributions, the belief distributions of all belief rules need to be weighted and averaged to obtain the final health status assessment result.

[0083] In the specific implementation: 1. Obtain raw data of battery pack operation: Obtain raw data of satellite lithium-ion battery packs 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 this data, removing outliers and missing values, and normalize the data to ensure data scale consistency of operating parameters across different battery packs for subsequent processing. 2. Use Evidence-Based Reasoning (ER) to fuse multi-source inconsistency indicators: Extract inconsistency indicators from the preprocessed data, including voltage differences, temperature differences, and capacity differences among individual cells. During this process, use the Evidence-Based Reasoning (ER) model to fuse multi-source inconsistency information, generating a comprehensive inconsistency assessment value by assigning weights to each indicator. The Evidence-Based Reasoning method can handle uncertainties from different data sources and generate a comprehensive assessment result through a belief degree distribution. 3. Construction of the Belief Rule Base (BRB): The Belief Rule Base (BRB) is a reasoning system based on expert knowledge, where rules are expressed in an "if-then" format. The core of BRB (Battery Rule Base) lies in expressing the relevance of each rule to the input data through "Belief Degree." Belief Degree refers to the system's confidence in a given evaluation result, i.e., the probability that the data conforms to a certain rule. For example, the input data may simultaneously activate multiple rules; the activation degree of each rule is characterized by its belief degree. The higher the belief degree, the greater the influence of that rule on the final evaluation result. 4. The reasoning principle of the Belief Rule Base: After obtaining the inconsistent evaluation values ​​of the battery pack, they are input into the Belief Rule Base. BRB performs reasoning based on the battery pack's performance indicators (such as capacity, discharge voltage, etc.) using predefined rules in the rule base. Each rule calculates its activation weight based on the input data and assigns weights through belief degree, generating multiple belief distributions. Subsequently, the activated rules are fused using evidence-based reasoning methods, ultimately outputting the evaluation result. The reasoning capability of the Belief Rule Base comes from its ability to simultaneously process multiple uncertain pieces of information and achieve automated reasoning through rule weight allocation. 5. Comprehensive Assessment of Battery Pack Health Status: During the reasoning process using the belief rule base, multiple different rules are activated based on the input data. The final assessment result is calculated by weighting the belief scores of multiple activated rules. The core role of belief score is to handle uncertainty, distributing the assessment result across multiple possible health states. Ultimately, the system integrates these belief score distributions into a comprehensive health status assessment of the battery pack. 6. Model Validation and Optimization: The model is validated using historical operational data from the satellite battery pack. Cross-validation is employed to evaluate the model's performance on different satellite battery packs, particularly its ability to accurately predict inconsistencies in individual battery cells and the overall performance of the battery pack. Optimal model parameters are determined using the validation set and adjusted and optimized based on the operational conditions of different battery packs.

[0084] This system comprehensively evaluates battery pack performance by combining individual cell inconsistencies with overall pack performance, covering multiple key indicators such as voltage, temperature, and capacity. It not only reflects individual cell differences but also assesses the overall health of the battery pack. Strong uncertainty handling capabilities: Employing Evidence-Based Reasoning (ER) methods, it fuses and evaluates multiple uncertainties through belief degree distribution, ensuring reliable evaluation results even with complex and uncertain data sources. This uncertainty handling capability allows the system to maintain high evaluation accuracy even under varying battery pack operating environments and diverse data sources. Automated reasoning and high adaptability: An automated health status evaluation system is built using a Belief Rule Base (BRB). Combining expert knowledge with actual data, the evaluation process is automated. The BRB system dynamically activates different rules based on input data and performs weighted fusion, ensuring highly adaptable evaluation results. Regardless of the type of battery pack or different operating conditions, the system can flexibly adjust rules to maintain accurate evaluations. Real-time monitoring and early warning functions: Supports real-time monitoring of the battery pack's health status. Through a model deployed in the Battery Management System (BMS), it can collect battery data in real time and perform dynamic evaluations. Once an abnormality occurs in the battery pack's health status, the system will issue a timely warning, helping operators to identify potential problems in advance and preventing premature battery failure or damage. This real-time evaluation and early warning function significantly improves the reliability and efficiency of battery management.

[0085] This embodiment acquires the overall indicators of the target battery pack within different collection intervals over a preset time period, and the individual indicators of each cell in the target battery pack within the same preset time period. A battery belief rule base is constructed, comprising multiple belief rules, each representing the mapping relationship between indicator ranges and belief distributions. Based on the overall and individual indicators, target belief rules corresponding to each collection interval are selected 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 pack is determined based on the target belief distribution. This solves the technical problem of low assessment accuracy in traditional battery pack health status assessment methods. Compared to existing technologies, this application can fully consider the impact of inconsistencies in individual indicators among cells within the battery pack on the overall health status of the battery pack. It can also comprehensively consider the overall performance indicators of the battery pack and gain a deeper understanding of the battery pack's health status by tracking and analyzing long-term operating data, thus effectively improving the accuracy of battery pack health status assessment.

[0086] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, please refer to Figure 3; step S40 further includes steps S401 to S404:

[0087] Step S401: The overall voltage, the overall current, the overall temperature, the bypass state, and the overall inconsistency are used as input data for the battery belief rule base.

[0088] Step S402: Based on the input data, rule weights, rule matching degree, and indicator importance weights, determine the rule activation weight of the target belief rule;

[0089] It should be noted that the specific formula for calculating the rule activation weight is as follows:

[0090] In the formula, θ k It is the weight of the belief rule (usually determined by historical data or set manually); α k It represents the degree of matching between the input data and the rule premise k (i.e., similarity, rule matching degree); δ k M refers to the importance weight of each indicator (i.e., indicator importance weight), M refers to the number of pieces of evidence involved in the reasoning (i.e., input data), and L refers to the total number of rules.

[0091] When the input data is close to the preconditions of the rule, the similarity is high, and the activation weight of the rule will also be high.

[0092] Step S403: Determine the target belief distribution corresponding to the target belief rule;

[0093] Step S404: Determine the health status assessment result of the target battery pack based on the rule activation weights and the target belief distribution.

[0094] In one embodiment, the step of determining the health status assessment result of the target battery pack based on 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 based on the rule activation weights and the target belief distribution; and determining the health status assessment result of the target battery pack based on the health confidence, the slight degradation confidence, and the severe degradation confidence.

[0095] When multiple belief rules are activated, the belief distributions of these belief rules need to be merged to obtain the final health state belief distribution. The specific merging method is as follows:

[0096] In the formula, βj ω represents the final belief distribution for health state j (e.g., "normal", "mild deterioration", "severe deterioration"), such as the confidence level for health, mild deterioration, and severe deterioration; i β is the rule activation weight of rule i; i,j is the initial belief distribution of rule i to state j; K is the normalization coefficient, used to ensure that the sum of belief values ​​is 1.

[0097] In one embodiment, the step of determining the health status assessment result of the target battery pack based on the health confidence level, the minor degradation confidence level, and the severe degradation confidence level includes: determining a health utility value, a minor degradation utility value, and a severe degradation utility value; determining a health assessment value based on the health confidence level and the health utility value; determining a minor degradation assessment value based on the minor degradation confidence level and the minor degradation utility value; and determining a severe degradation assessment value based on the severe degradation confidence level and the severe degradation utility value.

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

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

[0100] In the formula, N is the number of health states (in your case, there are 3 health states: normal, slightly deteriorated, and severely deteriorated, so N = 3); μ j It is the utility value for each health state. For example, for the "normal state", the health utility value is μ. 正常 =1; For the "slightly degraded" state, the slight degraded utility value is...

[0101] μ 轻微退化 =0.7; For the "severely degraded" state, μ 严重退化 =0.4; β j It is the confidence score for each health state, calculated through rule fusion, for example: β 正常 It is the health confidence level, β 轻微退化 It is a slightly degraded confidence level, β 严重退化 It is a severely degraded confidence level.

[0102] The above examples are only for understanding this application and do not constitute a limitation on the battery pack health status assessment method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0103] This application also provides a battery pack health status assessment device. Referring to Figure 4, the battery pack health status assessment device includes:

[0104] The acquisition module 10 is used to acquire the overall indicators of the target battery pack in different acquisition intervals within a preset time period, and to acquire the individual indicators of each cell in the target battery pack in different acquisition intervals within a preset time period.

[0105] The construction module 20 is used to construct a battery belief rule base, wherein the battery belief rule base includes multiple belief rules, and each belief rule is used to characterize the mapping relationship between the index range and the belief distribution;

[0106] The filtering module 30 is used to filter out the target belief rules corresponding to each collection interval from the battery belief rule library based on the overall index and the individual index.

[0107] The determination module 40 is used to determine the target belief distribution corresponding to the target belief rule, and to determine the health status assessment result of the target battery pack based on the target belief distribution.

[0108] The battery pack health status assessment device provided in this application, employing the battery pack health status assessment method described in the above embodiments, can solve the technical problem of low assessment accuracy commonly found 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 in this application are the same as those of the battery pack health status assessment method provided in the above embodiments, and other technical features in the battery pack health status assessment device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0109] This 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 to enable the at least one processor to perform the battery pack health status assessment method in Embodiment 1 above.

[0110] Referring to Figure 5 below, a schematic diagram of a battery pack health status assessment device suitable for implementing embodiments of this application is shown. The battery pack health status assessment device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The battery pack health status assessment device shown in Figure 5 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of this application.

[0111] As shown in Figure 5, the battery pack health status assessment device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the battery pack health status assessment device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected 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 I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the battery pack health assessment device to communicate wirelessly or wiredly with other devices to exchange data. Although a battery pack health assessment device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0112] According to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0113] The battery pack health status assessment device provided in this application, employing the battery pack health status assessment method described in the above embodiments, can solve the technical problem of low assessment accuracy commonly found 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 in this application are the same as those of the battery pack health status assessment method provided in the above embodiments, and other technical features of this battery pack health status assessment device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0114] It should be understood that the various parts disclosed in this application can be implemented using 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 suitable manner in one or more embodiments or examples.

[0115] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0116] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the battery pack health status assessment method in the above embodiments.

[0117] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. 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 conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0118] The aforementioned computer-readable storage medium may be included in the battery pack health status assessment device; or it may exist independently and not assembled into the battery pack health status assessment device.

[0119] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the battery pack health status assessment device, cause the battery pack health status assessment device to: (independent content).

[0120] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0122] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0123] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described battery pack health status assessment method. This solves the technical problem of low assessment accuracy commonly found in traditional battery pack health status assessment methods. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the battery pack health status assessment method provided in the above embodiments, and will not be elaborated upon here.

[0124] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the battery pack health status assessment method described above.

[0125] The computer program product provided in this 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 in this application are the same as those of the battery pack health status assessment method provided in the above embodiments, and will not be repeated here.

[0126] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

[0127] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for assessing the health status of a battery pack, wherein, The method includes: The overall indicators of the target battery pack are obtained within different collection intervals of a preset time period, and the individual indicators of each cell in the target battery pack are obtained within different collection intervals of a preset time period. A battery belief rule base is constructed, 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 individual index, target belief rules corresponding to each collection interval are selected from the battery belief rule base; Determine the target belief distribution corresponding to the target belief rule, and determine the health status assessment result of the target battery pack based on the target belief distribution.

2. The method as described in claim 1, wherein, The overall indicators include overall voltage, overall current, overall temperature, and bypass status. The step of selecting the target belief rule corresponding to each collection interval from the battery belief rule base based on the overall indicators and the individual cell indicators includes: The voltage inconsistency, current inconsistency, and temperature inconsistency of the target battery pack are determined based on the individual cell specifications of each cell in the target battery pack. Based on the voltage inconsistency, the current inconsistency, and the temperature inconsistency, a comprehensive inconsistency is determined. Based on the overall voltage, overall current, overall temperature, bypass status, and overall inconsistency, target belief rules corresponding to each acquisition interval are selected from the battery belief rule library.

3. The method as described in claim 2, wherein, The individual cell indicators include individual cell voltage, individual cell current, and individual cell temperature; wherein, the step of determining the voltage inconsistency, current inconsistency, and temperature inconsistency of the target battery pack based on the individual cell indicators of each cell in the target battery pack includes: The voltage inconsistency of the target battery pack is determined based on the maximum and minimum single-cell voltages in the single-cell indicators. The current inconsistency of the target battery pack is determined based on the maximum and minimum single-cell currents in the single-cell indicators. The temperature inconsistency of the target battery pack is determined based on the highest and lowest individual cell temperatures in the individual cell indicators.

4. The method of claim 2, wherein, The steps of determining the target belief distribution corresponding to the target belief rule and determining the health status assessment result of the target battery pack based on the target belief distribution include: The overall voltage, overall current, overall temperature, bypass state, and overall inconsistency are used as input data for the battery belief rule base. Based on the input data, rule weights, rule matching degree, and indicator importance weights, the rule activation weights of the target belief rules are determined. Determine the distribution of target beliefs corresponding to the aforementioned target belief rules; The health status assessment result of the target battery pack is determined based on the rule activation weights and the target belief distribution.

5. The method of claim 4, wherein, The step of determining the health status assessment result of the target battery pack based on the rule activation weights and target belief distribution includes: Based on the activation weights of the rules and the target belief distribution, determine the health confidence, slight degradation confidence, and severe degradation confidence in the final belief distribution; The health status assessment result of the target battery pack is determined based on the health confidence level, the slight degradation confidence level, and the severe degradation confidence level.

6. The method of claim 5, wherein, The step of determining the health status assessment result of the target battery pack based on the health confidence level, the slight degradation confidence level, and the severe degradation confidence level includes: Determine the health utility value, mild deterioration utility value, and severe deterioration utility value; The health assessment value is determined based on the health confidence level and the health utility value; The assessment value for minor degradation is determined based on the confidence level of minor degradation and the utility value of minor degradation; The severe degradation assessment value is determined based on the severe degradation confidence level and the severe degradation utility value; Based on the health assessment value, the slight degradation assessment value, and the severe degradation assessment value, the health status assessment result of the target battery pack is determined.

7. The method of claim 1, wherein, The steps of obtaining the overall indicators of the target battery pack within different collection intervals of a preset time period, and obtaining the individual indicators of each cell in the target battery pack within different collection intervals of a preset time period, include: The initial overall index of the target battery pack in different collection intervals within a preset time period is obtained from the satellite battery management system, and the initial individual index of each cell in the target battery pack in different collection intervals within a preset time period is obtained. The initial overall index and the initial individual index are cleaned to remove missing values, outliers and noise values, resulting in cleaned data. The cleaned data is normalized to obtain overall indicators and individual indicators.

8. A battery pack health status assessment device, wherein, The device includes: The acquisition module is used to acquire the overall indicators of the target battery pack in different acquisition intervals within a preset time period, and to acquire the individual indicators of each cell in the target battery pack in different acquisition intervals within a preset time period. A construction module is used to construct 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; The filtering module is used to filter out the target belief rules corresponding to each collection interval from the battery belief rule base based on the overall index and the individual index. The determination module is used to determine the target belief distribution corresponding to the target belief rule, and to determine the health status assessment result of the target battery pack based on the target belief distribution.

9. A battery pack health status assessment device, wherein, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the battery pack health status assessment method as described in any one of claims 1 to 7.

10. A storage medium, wherein, 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, it implements the steps of the battery pack health status assessment method as described in any one of claims 1 to 7.

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