Method and device for evaluating comprehensive state of energy storage battery and electronic equipment

Abstract

The invention provides an energy storage battery comprehensive state evaluation method and device and electronic equipment, and relates to the technical field of battery performance diagnos.The method comprises the steps that energy storage battery static data and energy storage battery dynamic data are obtained; determining battery health information, charging behavior information, discharging behavior information, consistency information and fault information based on the energy storage battery static data and the energy storage battery dynamic data; weighting based on the battery health information, the charging behavior information, the discharging behavior information, the consistency information and the fault information to obtain a comprehensive state grade of the energy storage battery; and generating an energy storage battery adjustment strategy based on the comprehensive state grade of the energy storage battery. The abnormal condition of the battery can be found in time, powerful support is provided for formulating an effective adjustment strategy, the service life of the battery is prolonged, and the safety and reliability of a battery system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery performance diagnosis, and in particular to a method, device and electronic equipment for evaluating the comprehensive state of an energy storage battery. Background Art

[0002] With the rapid development of renewable energy, energy storage batteries, as its core components, play a vital role in the power system. They can not only effectively balance the contradiction between supply and demand and improve the operating efficiency of the system, but also provide stable and reliable energy storage support for renewable energy. However, the health and safety performance of energy storage batteries are directly related to the stability and life of the entire system, and even to the stability of operations and public safety.

[0003] Although there are many battery monitoring solutions on the market, most of them focus on single-dimensional health status assessment or safety warning. For example, some solutions only focus on single parameters such as the remaining capacity or internal resistance of the battery to judge the health of the battery; while other solutions focus on parameters such as battery temperature or voltage to prevent possible safety risks. However, this single-dimensional evaluation method often cannot fully and accurately reflect the actual performance and potential problems of the battery.

[0004] In addition, due to the complexity and diversity of energy storage battery systems, different types of batteries, different application scenarios, and different operating conditions may have different effects on battery health and safety.

[0005] Therefore, a method, device and electronic equipment for evaluating the comprehensive status of an energy storage battery are proposed. Summary of the invention

[0006] This specification provides a method, device and electronic device for evaluating the comprehensive status of an energy storage battery, which can promptly detect abnormal conditions of the battery, provide strong support for formulating effective adjustment strategies, help extend the service life of the battery, and improve the safety and reliability of the battery system.

[0007] This specification provides a method for evaluating the comprehensive state of an energy storage battery, including:

[0008] Obtain static data and dynamic data of energy storage batteries;

[0009] Determine battery health information, charging behavior information, discharging behavior information, consistency information, and fault information based on the energy storage battery static data and the energy storage battery dynamic data;

[0010] Obtaining a comprehensive state level of the energy storage battery based on the battery health information, the charging behavior information, the discharging behavior information, the consistency information, and the fault information;

[0011] Based on the comprehensive status level of the energy storage battery, an energy storage battery adjustment strategy is generated.

[0012] Optionally, determining the battery health information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0013] Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain a SoC value, and determine the SoC interval corresponding to the current energy storage battery based on the SoC value;

[0014] Filter the static data of the energy storage battery and the dynamic data of the energy storage battery based on the SoC interval corresponding to the current energy storage battery to obtain first filtered data;

[0015] A SoH index is determined based on the first screening data, and the battery health information is obtained by fitting the SoH index mapping using a polynomial.

[0016] Optionally, determining the charging behavior information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0017] The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the first preset key field to obtain the second screening data; the second screening data includes the number of charging times A1 when the charging temperature is higher than the upper limit value of the charging standard temperature, the number of charging times A2 when the charging temperature is lower than the lower limit value of the charging standard temperature, the number of charging times A3 when the charging current is greater than 0.5C, the number of charging times A4 when the charging end SoC is greater than the first user-set threshold, and the total number of charging times A5. all ;

[0018] Based on the second screening data, high temperature charging information, low temperature charging information, fast charging information, and overcharge charging information are determined, including: the number of charging times A1 based on the charging temperature being higher than the upper limit of the charging standard temperature, the total number of charging times A all Determine the high temperature charging information B1, specifically:

[0019] B1=(1-A1 / A all )*100

[0020] The number of charging times A2 based on the charging temperature being lower than the lower limit of the charging standard temperature, the total number of charging times A all Determine the low temperature charging information B2, specifically:

[0021] B2=(1-A2 / A all )*100

[0022] Based on the charging current being greater than 0.5C, the charging times A3 and the total charging times A allDetermine the fast charging information B3, specifically:

[0023] B3=(1-A3 / A all )*100

[0024] Based on the charging end SoC being greater than the charging number A4 of the user-set threshold, the total charging number A all Determine the overcharge information B4, specifically:

[0025] B4=(1-A4 / A all )*100

[0026] The high temperature charging information, the low temperature charging information, the fast charging information, and the overcharge charging information are weighted by a tomographic analysis method to obtain charging behavior information.

[0027] Optionally, the determining the discharge behavior information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0028] The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the second preset key field to obtain third screening data; the third screening data includes the number of discharges C1 when the discharge temperature is higher than the upper limit of the discharge standard temperature, the number of discharges C2 when the discharge temperature is lower than the lower limit of the discharge standard temperature, the number of discharges C3 when the discharge exceeds the second user-set threshold, the number of discharges C4 when the SoC is less than the third user-set threshold at the end of the discharge, and the total number of discharges C all ;

[0029] Based on the third screening data, high temperature discharge information D1, low temperature discharge information D2, discharge depth information D3, and over discharge information D4 are determined, including: the number of discharges C1 based on the discharge temperature being higher than the upper limit of the discharge standard temperature, the total number of discharges C all Determine the high temperature discharge information D1, specifically:

[0030] D1=(1-C1 / C all )*100

[0031] Based on the discharge number C2 where the discharge temperature is lower than the lower limit of the discharge standard temperature, the total discharge number C all Determine the low temperature discharge information D2, specifically:

[0032] D2=(1-C2 / C all )*100

[0033] The number of discharges C3 and the total number of discharges C3 based on the discharge depth of the discharge process exceeding the second user-set threshold all Determine the discharge depth information D3, specifically:

[0034] D3=(1-C3 / C all )*100

[0035] Based on the discharge number C4 and the total discharge number C4 of the SoC being less than the third user-set threshold value at the end of the discharge, all Determine the over-discharge information D4, specifically:

[0036] D4=(1-C4 / C all )*100

[0037] The high-temperature discharge information D1, the low-temperature discharge information D2, the discharge depth information D3, and the over-discharge information D4 are weighted by a tomographic analysis method to obtain discharge behavior information.

[0038] Optionally, the determining the consistency information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0039] Based on the static data of the energy storage battery and the dynamic data of the energy storage battery, determine the median value of the voltage of all the battery cells in each frame of data and the median value of the temperature of all the battery cells in each frame of data;

[0040] Performing Euclidean distance calculation on the voltage of each battery cell during the charging process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the charging process;

[0041] Performing Euclidean distance calculation on the temperature of each battery cell during the charging process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the charging process;

[0042] Performing Euclidean distance calculation on the voltage of each battery cell during the discharge process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the discharge process;

[0043] Performing Euclidean distance calculation on the temperature of each battery cell during the discharge process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the discharge process;

[0044] Determine the data density value of the charging process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the charging process and an average Euclidean distance value of the temperature of each battery cell in the charging process;

[0045] Determine the data density value of the discharge process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the discharge process and an average Euclidean distance value of the temperature of each battery cell in the discharge process;

[0046] Determine the LOF value of each battery cell charging process based on the data density value of each battery cell charging process, and use the LOF value of each battery cell charging process to respectively determine the voltage consistency information of the charging process and the temperature consistency information of the charging process;

[0047] Determine the LOF value of each battery cell discharge process based on the data density value of each battery cell discharge process, and use the LOF value of each battery cell discharge process to respectively determine the voltage consistency information of the discharge process and the temperature consistency information of the discharge process;

[0048] The consistency information is obtained by weighting the voltage consistency information of the charging process, the temperature consistency information of the charging process, the voltage consistency information of the discharging process, and the temperature consistency information of the discharging process through a tomographic analysis method.

[0049] Optionally, the determining the fault information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0050] Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain the number of occurrences of fault level L, the deduction value of fault level L, and the influence coefficient of duration of fault level L;

[0051] The fault information is determined based on the number of occurrences of the fault level L, the deduction value of the fault level L, and the influence coefficient of the duration of the fault level L, specifically:

[0052]

[0053] Where D is the fault information, N is the fault level, and n L is the number of occurrences of fault level L, P(L) is the deduction value of fault level L, f α(L) is the influence coefficient of fault level L duration.

[0054] Optionally, the comprehensive state level of the energy storage battery obtained by weighting the battery health information, the charging behavior information, the discharging behavior information, the consistency information, and the fault information includes:

[0055] The energy storage battery comprehensive status information HLB is obtained by weighting the battery health information SoH Score, the charging behavior information ChargeScore, the discharging behavior information Discharge Score, the consistency information Consistency Score, and the fault information Fault Score through the entropy weight method. Specifically: HLB = ω1*SoH Score+ω2*Charge Score+ω3*Discharge Score+ω4*Consistency Score+ω5*Fault Score

[0056] Among them, ω 1、 ω 2、 ω 3、 ω 4、 ω5 are all weight values;

[0057] The comprehensive state level of the energy storage battery is determined based on the comprehensive state information of the energy storage battery.

[0058] This specification provides a device for evaluating the comprehensive state of an energy storage battery, comprising:

[0059] An acquisition module is used to acquire static data and dynamic data of energy storage batteries;

[0060] An analysis module, used to determine battery health information, charging behavior information, discharging behavior information, consistency information, and fault information based on the energy storage battery static data and the energy storage battery dynamic data;

[0061] A weighting module, used for obtaining a comprehensive state level of the energy storage battery by weighting the battery health information, the charging behavior information, the discharging behavior information, the consistency information, and the fault information;

[0062] A generation module is used to generate an energy storage battery adjustment strategy based on the comprehensive status level of the energy storage battery.

[0063] Optionally, the analysis module includes:

[0064] Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain a SoC value, and determine the SoC interval corresponding to the current energy storage battery based on the SoC value;

[0065] Filter the static data of the energy storage battery and the dynamic data of the energy storage battery based on the SoC interval corresponding to the current energy storage battery to obtain first filtered data;

[0066] A SoH index is determined based on the first screening data, and the battery health information is obtained by fitting the SoH index mapping using a polynomial.

[0067] Optionally, the analysis module includes:

[0068] The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the first preset key field to obtain the second screening data; the second screening data includes the number of charging times A1 when the charging temperature is higher than the upper limit value of the charging standard temperature, the number of charging times A2 when the charging temperature is lower than the lower limit value of the charging standard temperature, the number of charging times A3 when the charging current is greater than 0.5C, the number of charging times A4 when the charging end SoC is greater than the first user-set threshold, and the total number of charging times A5. all ;

[0069] Based on the second screening data, high temperature charging information, low temperature charging information, fast charging information, and overcharge charging information are determined, including: the number of charging times A1 based on the charging temperature being higher than the upper limit of the charging standard temperature, the total number of charging times A all Determine the high temperature charging information B1, specifically:

[0070] B1=(1-A1 / A all )*100

[0071] The number of charging times A2 based on the charging temperature being lower than the lower limit of the charging standard temperature, the total number of charging times A all Determine the low temperature charging information B2, specifically:

[0072] B2=(1-A2 / A all )*100

[0073] Based on the charging current being greater than 0.5C, the charging times A3 and the total charging times A all Determine the fast charging information B3, specifically:

[0074] B3=(1-A3 / A all )*100

[0075] Based on the charging end SoC being greater than the charging number A4 of the user-set threshold, the total charging number A all Determine the overcharge information B4, specifically:

[0076] B4=(1-A4 / A all )*100

[0077] The high temperature charging information, the low temperature charging information, the fast charging information, and the overcharge charging information are weighted by a tomographic analysis method to obtain charging behavior information.

[0078] Optionally, the analysis module includes:

[0079] The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the second preset key field to obtain third screening data; the third screening data includes the number of discharges C1 when the discharge temperature is higher than the upper limit of the discharge standard temperature, the number of discharges C2 when the discharge temperature is lower than the lower limit of the discharge standard temperature, the number of discharges C3 when the discharge exceeds the second user-set threshold, the number of discharges C4 when the SoC is less than the third user-set threshold at the end of the discharge, and the total number of discharges C all ;

[0080] Based on the third screening data, high temperature discharge information D1, low temperature discharge information D2, discharge depth information D3, and over discharge information D4 are determined, including: the number of discharges C1 based on the discharge temperature being higher than the upper limit of the discharge standard temperature, the total number of discharges C all Determine the high temperature discharge information D1, specifically:

[0081] D1=(1-C1 / C all )*100

[0082] Based on the discharge number C2 where the discharge temperature is lower than the lower limit of the discharge standard temperature, the total discharge number C all Determine the low temperature discharge information D2, specifically:

[0083] D2=(1-C2 / C all )*100

[0084] The number of discharges C3 and the total number of discharges C3 based on the discharge depth of the discharge process exceeding the second user-set threshold all Determine the discharge depth information D3, specifically:

[0085] D3=(1-C3 / C all )*100

[0086] Based on the discharge number C4 and the total discharge number C4 of the SoC being less than the third user-set threshold value at the end of the discharge, all Determine the over-discharge information D4, specifically:

[0087] D4=(1-C4 / C all )*100

[0088] The high-temperature discharge information D1, the low-temperature discharge information D2, the discharge depth information D3, and the over-discharge information D4 are weighted by a tomographic analysis method to obtain discharge behavior information.

[0089] Optionally, the analysis module includes:

[0090] Based on the static data of the energy storage battery and the dynamic data of the energy storage battery, determine the median value of the voltage of all the battery cells in each frame of data and the median value of the temperature of all the battery cells in each frame of data;

[0091] Performing Euclidean distance calculation on the voltage of each battery cell during the charging process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the charging process;

[0092] Performing Euclidean distance calculation on the temperature of each battery cell during the charging process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the charging process;

[0093] Performing Euclidean distance calculation on the voltage of each battery cell during the discharge process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the discharge process;

[0094] Performing Euclidean distance calculation on the temperature of each battery cell during the discharge process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the discharge process;

[0095] Determine the data density value of the charging process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the charging process and an average Euclidean distance value of the temperature of each battery cell in the charging process;

[0096] Determine the data density value of the discharge process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the discharge process and an average Euclidean distance value of the temperature of each battery cell in the discharge process;

[0097] Determine the LOF value of each battery cell charging process based on the data density value of each battery cell charging process, and use the LOF value of each battery cell charging process to respectively determine the voltage consistency information of the charging process and the temperature consistency information of the charging process;

[0098] Determine the LOF value of each battery cell discharge process based on the data density value of each battery cell discharge process, and use the LOF value of each battery cell discharge process to respectively determine the voltage consistency information of the discharge process and the temperature consistency information of the discharge process;

[0099] The consistency information is obtained by weighting the voltage consistency information of the charging process, the temperature consistency information of the charging process, the voltage consistency information of the discharging process, and the temperature consistency information of the discharging process through a tomographic analysis method.

[0100] Optionally, the analysis module includes:

[0101] Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain the number of occurrences of fault level L, the deduction value of fault level L, and the influence coefficient of duration of fault level L;

[0102] The fault information is determined based on the number of occurrences of the fault level L, the deduction value of the fault level L, and the influence coefficient of the duration of the fault level L, specifically:

[0103]

[0104] Where D is the fault information, N is the fault level, and n L is the number of occurrences of fault level L, P(L) is the deduction value of fault level L, f α(L) is the influence coefficient of fault level L duration.

[0105] Optionally, the weighting module includes:

[0106] The energy storage battery comprehensive status information HLB is obtained by weighting the battery health information SoH Score, the charging behavior information ChargeScore, the discharging behavior information Discharge Score, the consistency information Consistency Score, and the fault information Fault Score through the entropy weight method. Specifically: HLB = ω1*SoH Score+ω2*Charge Score+ω3*Discharge Score+ω4*Consistency Score+ω5*Fault Score

[0107] Among them, ω 1、 ω 2、 ω 3、 ω 4、 ω5 are all weight values;

[0108] The comprehensive state level of the energy storage battery is determined based on the comprehensive state information of the energy storage battery.

[0109] This specification also provides an electronic device, wherein the electronic device includes:

[0110] processor; and,

[0111] A memory storing computer executable instructions, which when executed cause the processor to perform any of the above methods.

[0112] The present specification also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, any of the above methods is implemented.

[0113] In the present invention, by comprehensively considering multiple dimensions such as battery health information, charging behavior information, discharging behavior information, consistency information and fault information, the entropy weight method is used to scientifically and rationally determine the weight of each indicator, thereby weighting to obtain the comprehensive status information of the energy storage battery. This method not only takes into account the current state of the battery, but also deeply analyzes the charging and discharging behavior of the battery and the consistency between the cells, and combines the fault information at the same time, which can more accurately reflect the actual performance and potential problems of the battery. Through this method, abnormal conditions of the battery can be discovered in time, providing strong support for the formulation of effective adjustment strategies, helping to extend the service life of the battery and improve the safety and reliability of the battery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0115] Figure 1 A schematic diagram of the principle of a method for evaluating the comprehensive state of an energy storage battery provided in an embodiment of this specification;

[0116] Figure 2 A schematic diagram of the structure of a device for evaluating the comprehensive state of an energy storage battery provided in an embodiment of this specification;

[0117] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification;

[0118] Figure 4 A schematic diagram of a computer-readable medium provided in accordance with an embodiment of the present specification. DETAILED DESCRIPTION

[0119] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0120] The following is combined with Figure 1-4The exemplary embodiments of the present invention are described more fully. However, the exemplary embodiments can be implemented in various forms, and it should not be understood that the present invention is limited to the embodiments set forth herein. On the contrary, providing these exemplary embodiments can make the present invention more comprehensive and complete, and it is more convenient to fully convey the inventive concept to those skilled in the art. The same reference numerals in the figures represent the same or similar elements, components or parts, and thus their repeated description will be omitted.

[0121] Under the premise of being consistent with the technical concept of the present invention, the features, structures, characteristics or other details described in a specific embodiment do not exclude that they can be combined in one or more other embodiments in a suitable manner.

[0122] In the description of specific embodiments, the features, structures, characteristics or other details described in the present invention are intended to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can practice the technical solutions of the present invention without one or more of the specific features, structures, characteristics or other details.

[0123] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0124] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0125] The term "and / or" or "and / or" includes all combinations of any one or more of the associated listed items.

[0126] Figure 1 A schematic diagram of a method for evaluating the comprehensive state of an energy storage battery provided in an embodiment of this specification, the method may include:

[0127] S110: Acquire static data and dynamic data of the energy storage battery;

[0128] Optionally, the S110 includes:

[0129] Obtain static data and dynamic data of energy storage batteries within a preset time;

[0130] The static data of the energy storage battery and the dynamic data of the energy storage battery are cleaned to obtain valid static data of the energy storage battery and valid dynamic data of the energy storage battery.

[0131] In the specific implementation of this specification, the static data of the energy storage battery refers to some inherent properties or parameters of the battery itself at a specific time point or time period, such as battery capacity, voltage level, battery type, etc. The static data of the energy storage battery is obtained from the battery management system (BMS) by accessing the database provided by the manufacturer of the energy storage battery, consulting the product manual or contacting technical support personnel.

[0132] The dynamic data of energy storage batteries refers to the parameters that change over time during the operation of the battery, such as charge and discharge current, voltage, temperature, SOC (remaining power), etc. Sensors or data acquisition equipment are installed to monitor the operating status of energy storage batteries in real time, and the dynamic data of energy storage batteries are transmitted to the data recording system or cloud platform.

[0133] Use statistical processing methods to locate the default values ​​in the data set, use algorithms such as the K nearest neighbor algorithm to determine the normal values ​​nearby, and replace the default values ​​with the most frequent normal values. Use methods such as the Laida criterion method to locate the outliers in the data set, use algorithms such as the K nearest neighbor algorithm to determine the normal values ​​nearby, and replace the outliers with these normal values. According to the different category characteristics of the energy storage battery data (such as current, voltage, temperature, SOC, and power, etc.), determine the unreasonable data in the data set after replacing the default values ​​and outliers, and replace them according to the normal values ​​before and after, or replace them according to the mutation threshold of the data. In the process of cleaning the data, introduce a data quality detection mechanism to analyze the quality of the collected data and eliminate errors and abnormal data.

[0134] S120: Determine battery health information, charging behavior information, discharging behavior information, consistency information, and fault information based on the energy storage battery static data and the energy storage battery dynamic data;

[0135] Optionally, determining the battery health information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0136] Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain a SoC value, and determine the SoC interval corresponding to the current energy storage battery based on the SoC value;

[0137] Filter the static data of the energy storage battery and the dynamic data of the energy storage battery based on the SoC interval corresponding to the current energy storage battery to obtain first filtered data;

[0138] A SoH index is determined based on the first screening data, and the battery health information is obtained by fitting the SoH index mapping using a polynomial.

[0139] In a specific implementation of the present specification, the SoC value is calculated by a specific algorithm (such as coulomb counting method, open circuit voltage method, etc.) using the dynamic data (such as current, voltage, temperature, etc.) and static data (such as battery capacity, etc.) of the energy storage battery. According to the range of the SoC value, it is divided into different intervals, for example: [0%, 20%], [20%, 40%], [40%, 60%], [60%, 80%], [80%, 100%]. Determine the interval to which the current SoC value belongs. According to the current SoC interval, the static data and dynamic data of the energy storage battery are screened, and the data related to the current SoC interval or collected within the interval are selected to obtain a data set closely related to the current SoC interval, that is, the first screening data. Using the first screening data, the SoH indicator is calculated by a specific algorithm or model (such as a capacity attenuation model, an internal resistance growth model, etc.). The SoH indicator generally indicates the degree of performance attenuation of the battery relative to a new battery, and its value range is generally between 0 and 1. The SoH indicator is polynomially fitted to obtain a mapping relationship between SoH and battery health information. According to the polynomial fitting results, the SoH indicator is mapped to specific battery health information (such as remaining life, performance degradation, etc.).

[0140] Optionally, determining the charging behavior information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0141] The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the first preset key field to obtain the second screening data; the second screening data includes the number of charging times A1 when the charging temperature is higher than the upper limit value of the charging standard temperature, the number of charging times A2 when the charging temperature is lower than the lower limit value of the charging standard temperature, the number of charging times A3 when the charging current is greater than 0.5C, the number of charging times A4 when the charging end SoC is greater than the first user-set threshold, and the total number of charging times A5. all ;

[0142] Based on the second screening data, high temperature charging information, low temperature charging information, fast charging information, and overcharge charging information are determined, including: the number of charging times A1 based on the charging temperature being higher than the upper limit of the charging standard temperature, the total number of charging times A all Determine the high temperature charging information B1, specifically:

[0143] B1=(1-A1 / A all )*100

[0144] The number of charging times A2 based on the charging temperature being lower than the lower limit of the charging standard temperature, the total number of charging times Aall Determine the low temperature charging information B2, specifically:

[0145] B2=(1-A2 / A all )*100

[0146] Based on the charging current being greater than 0.5C, the charging times A3 and the total charging times A all Determine the fast charging information B3, specifically:

[0147] B3=(1-A3 / A all )*100

[0148] Based on the charging end SoC being greater than the charging number A4 of the user-set threshold, the total charging number A all Determine the overcharge information B4, specifically:

[0149] B4=(1-A4 / A all )*100

[0150] The high temperature charging information, the low temperature charging information, the fast charging information, and the overcharge charging information are weighted by a tomographic analysis method to obtain charging behavior information.

[0151] In a specific implementation of this specification, the static data and dynamic data of the energy storage battery are screened using the first preset key field (such as charging time, charging temperature, charging current, SoC at the end of charging, etc.) to obtain second screened data.

[0152] Optionally, the determining the discharge behavior information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0153] The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the second preset key field to obtain third screening data; the third screening data includes the number of discharges C1 when the discharge temperature is higher than the upper limit of the discharge standard temperature, the number of discharges C2 when the discharge temperature is lower than the lower limit of the discharge standard temperature, the number of discharges C3 when the discharge exceeds the second user-set threshold, the number of discharges C4 when the SoC is less than the third user-set threshold at the end of the discharge, and the total number of discharges C all ;

[0154] Based on the third screening data, high temperature discharge information D1, low temperature discharge information D2, discharge depth information D3, and over discharge information D4 are determined, including: the number of discharges C1 based on the discharge temperature being higher than the upper limit of the discharge standard temperature, the total number of discharges C all Determine the high temperature discharge information D1, specifically:

[0155] D1=(1-C1 / Call )*100

[0156] Based on the discharge number C2 where the discharge temperature is lower than the lower limit of the discharge standard temperature, the total discharge number C all Determine the low temperature discharge information D2, specifically:

[0157] D2=(1-C2 / C all )*100

[0158] The number of discharges C3 and the total number of discharges C3 based on the discharge depth of the discharge process exceeding the second user-set threshold all Determine the discharge depth information D3, specifically:

[0159] D3=(1-C3 / C all )*100

[0160] Based on the discharge number C4 and the total discharge number C4 of the SoC being less than the third user-set threshold value at the end of the discharge, all Determine the over-discharge information D4, specifically:

[0161] D4=(1-C4 / C all )*100

[0162] The high-temperature discharge information D1, the low-temperature discharge information D2, the discharge depth information D3, and the over-discharge information D4 are weighted by a tomographic analysis method to obtain discharge behavior information.

[0163] In a specific implementation of the present specification, the second preset key field (such as discharge time, discharge temperature, discharge current, SoC at the end of discharge, etc.) is used to screen the static data and dynamic data of the energy storage battery.

[0164] Optionally, the determining the consistency information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0165] Based on the static data of the energy storage battery and the dynamic data of the energy storage battery, determine the median value of the voltage of all the battery cells in each frame of data and the median value of the temperature of all the battery cells in each frame of data;

[0166] Performing Euclidean distance calculation on the voltage of each battery cell during the charging process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the charging process;

[0167] Performing Euclidean distance calculation on the temperature of each battery cell during the charging process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the charging process;

[0168] Performing Euclidean distance calculation on the voltage of each battery cell during the discharge process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the discharge process;

[0169] Performing Euclidean distance calculation on the temperature of each battery cell during the discharge process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the discharge process;

[0170] Determine the data density value of the charging process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the charging process and an average Euclidean distance value of the temperature of each battery cell in the charging process;

[0171] Determine the data density value of the discharge process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the discharge process and an average Euclidean distance value of the temperature of each battery cell in the discharge process;

[0172] Determine the LOF value of each battery cell charging process based on the data density value of each battery cell charging process, and use the LOF value of each battery cell charging process to respectively determine the voltage consistency information of the charging process and the temperature consistency information of the charging process;

[0173] Determine the LOF value of each battery cell discharge process based on the data density value of each battery cell discharge process, and use the LOF value of each battery cell discharge process to respectively determine the voltage consistency information of the discharge process and the temperature consistency information of the discharge process;

[0174] The consistency information is obtained by weighting the voltage consistency information of the charging process, the temperature consistency information of the charging process, the voltage consistency information of the discharging process, and the temperature consistency information of the discharging process through a tomographic analysis method.

[0175] In the specific implementation of this specification, the Local Outlier Factor (LOF) algorithm is used to calculate the data density value of each battery cell during the charging process, combining the average voltage and temperature of each battery cell during the charging process, which reflects the local density of the battery cell data relative to other battery cell data. Similarly, the data density value of each battery cell during the discharge process is calculated.

[0176] Based on the data density value of each battery cell during the charging process, its LOF value is calculated. The higher the LOF value, the more isolated the data of the battery cell is compared with other batteries, and there may be abnormalities. Using these LOF values, the voltage consistency information and temperature consistency information of the charging process are determined respectively. Similarly, the discharge process is calculated to obtain the voltage consistency information and temperature consistency information of the discharge process.

[0177] Optionally, the determining the fault information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes:

[0178] Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain the number of occurrences of fault level L, the deduction value of fault level L, and the influence coefficient of duration of fault level L;

[0179] The fault information is determined based on the number of occurrences of the fault level L, the deduction value of the fault level L, and the influence coefficient of the duration of the fault level L, specifically:

[0180]

[0181] Where D is the fault information, N is the fault level, and n L is the number of occurrences of fault level L, P(L) is the deduction value of fault level L, f α(L) is the influence coefficient of fault level L duration.

[0182] In the specific implementation of this specification, the fault level L occurs times n L represents the total number of occurrences of fault level L in the data set; fault level L penalty value P(L) represents the penalty value corresponding to fault level L, which is usually set according to the severity of the fault; fault level L duration influence coefficient f α(L) It represents the influence coefficient of fault level L duration on the overall fault assessment. This coefficient may take into account the impact of the fault duration on battery performance or life.

[0183] S130: Obtaining a comprehensive state level of the energy storage battery by weighting the battery health information, the charging behavior information, the discharging behavior information, the consistency information, and the fault information;

[0184] Optionally, the S130 includes:

[0185] The energy storage battery comprehensive status information HLB is obtained by weighting the battery health information SoH Score, the charging behavior information ChargeScore, the discharging behavior information Discharge Score, the consistency information Consistency Score, and the fault information Fault Score through the entropy weight method. Specifically: HLB = ω1*SoH Score+ω2*Charge Score+ω3*Discharge Score+ω4*Consistency Score+ω5*Fault Score

[0186] Among them, ω1、 ω 2、 ω 3、 ω 4、 ω5 are all weight values;

[0187] The comprehensive state level of the energy storage battery is determined based on the comprehensive state information of the energy storage battery.

[0188] In the specific implementation of this specification, the entropy weight method is an objective weighting method, which determines the weight of each evaluation indicator according to its degree of variation. The greater the degree of variation of the indicator, the greater the amount of information it provides and the greater the impact on the comprehensive evaluation, so it should be given a greater weight.

[0189] After obtaining the comprehensive status information HLB, the comprehensive status level of the energy storage battery can be determined according to the value of HLB. This is usually achieved by setting a series of thresholds, such as:

[0190] If HLB>=90, the overall status grade is A (excellent)

[0191] If 80<=HLB<90, the overall status grade is B (good)

[0192] If 70<=HLB<80, the overall status grade is C (average)

[0193] If HLB < 70, the overall status grade is D (poor)

[0194] These thresholds can be adjusted according to the actual application scenario and the specific conditions of the battery.

[0195] S140: Generate an energy storage battery adjustment strategy based on the comprehensive status level of the energy storage battery.

[0196] In the specific implementation of this specification, when the comprehensive status level is Class A (excellent), it means that the battery is in good condition and no immediate action is required. Therefore, continue the current operation and maintenance strategy and maintain regular monitoring and inspection. When the comprehensive status level is Class B (good), it means that the battery is in good condition, but the performance can be further improved and the life can be extended by optimizing the operation and maintenance strategy. Therefore, strengthen preventive maintenance and optimize the charging and discharging strategy. When the comprehensive status level is Class C (average), it means that the battery performance begins to decline and requires closer attention and timely maintenance measures. Therefore, more frequent monitoring and maintenance are required, and performance recovery treatment (such as balanced charging) of some batteries is considered. When the comprehensive status level is Class D (poor), it means that the battery is in poor condition and may affect system performance and safety. Action needs to be taken as soon as possible. Therefore, detailed troubleshooting is required immediately, and replacement of some or all batteries is considered to optimize the system configuration.

[0197] According to the formulated adjustment strategy, perform corresponding operation and maintenance operations, such as replacing batteries, adjusting charging strategies, etc. After implementing the adjustment strategy, continue to closely monitor the performance of the battery system to ensure the effectiveness of the adjustment measures. Collect data after the implementation of the adjustment strategy and evaluate the effectiveness of the strategy. Based on the feedback results, fine-tune the adjustment strategy when necessary to continuously optimize the performance and safety of the battery system. In the process of formulating and implementing the adjustment strategy, identify possible risk points, such as battery failure, system downtime, etc. Develop corresponding risk mitigation measures, such as the preparation of backup batteries, system redundancy design, etc., to ensure that risks can be responded to quickly when they occur.

[0198] In the present invention, by comprehensively considering multiple dimensions such as battery health information, charging behavior information, discharging behavior information, consistency information and fault information, the entropy weight method is used to scientifically and rationally determine the weight of each indicator, thereby weighting to obtain the comprehensive status information of the energy storage battery. This method not only takes into account the current state of the battery, but also deeply analyzes the charging and discharging behavior of the battery and the consistency between the cells, and combines the fault information at the same time, which can more accurately reflect the actual performance and potential problems of the battery. Through this method, abnormal conditions of the battery can be discovered in time, providing strong support for the formulation of effective adjustment strategies, helping to extend the service life of the battery and improve the safety and reliability of the battery system.

[0199] Figure 2 A schematic diagram of a device for evaluating the comprehensive state of an energy storage battery provided in an embodiment of this specification may include:

[0200] An acquisition module 10 is used to acquire static data and dynamic data of the energy storage battery;

[0201] An analysis module 20, configured to determine battery health information, charging behavior information, discharging behavior information, consistency information, and fault information based on the energy storage battery static data and the energy storage battery dynamic data;

[0202] A weighting module 30, configured to obtain a comprehensive state level of the energy storage battery by weighting the battery health information, the charging behavior information, the discharging behavior information, the consistency information, and the fault information;

[0203] The generating module 40 is used to generate an energy storage battery adjustment strategy based on the comprehensive status level of the energy storage battery.

[0204] Optionally, the analysis module 20 includes:

[0205] Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain a SoC value, and determine the SoC interval corresponding to the current energy storage battery based on the SoC value;

[0206] Filter the static data of the energy storage battery and the dynamic data of the energy storage battery based on the SoC interval corresponding to the current energy storage battery to obtain first filtered data;

[0207] A SoH index is determined based on the first screening data, and the battery health information is obtained by fitting the SoH index mapping using a polynomial.

[0208] Optionally, the analysis module 20 includes:

[0209] The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the first preset key field to obtain the second screening data; the second screening data includes the number of charging times A1 when the charging temperature is higher than the upper limit value of the charging standard temperature, the number of charging times A2 when the charging temperature is lower than the lower limit value of the charging standard temperature, the number of charging times A3 when the charging current is greater than 0.5C, the number of charging times A4 when the charging end SoC is greater than the first user-set threshold, and the total number of charging times A5. all ;

[0210] Based on the second screening data, high temperature charging information, low temperature charging information, fast charging information, and overcharge charging information are determined, including: the number of charging times A1 based on the charging temperature being higher than the upper limit of the charging standard temperature, the total number of charging times A all Determine the high temperature charging information B1, specifically:

[0211] B1=(1-A1 / A all )*100

[0212] The number of charging times A2 based on the charging temperature being lower than the lower limit of the charging standard temperature, the total number of charging times A all Determine the low temperature charging information B2, specifically:

[0213] B2=(1-A2 / A all )*100

[0214] Based on the charging current being greater than 0.5C, the charging times A3 and the total charging times A all Determine the fast charging information B3, specifically:

[0215] B3=(1-A3 / A all )*100

[0216] Based on the charging end SoC being greater than the charging number A4 of the user-set threshold, the total charging number A all Determine the overcharge information B4, specifically:

[0217] B4=(1-A4 / A all )*100

[0218] The high temperature charging information, the low temperature charging information, the fast charging information, and the overcharge charging information are weighted by a tomographic analysis method to obtain charging behavior information.

[0219] Optionally, the analysis module 20 includes:

[0220] The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the second preset key field to obtain third screening data; the third screening data includes the number of discharges C1 when the discharge temperature is higher than the upper limit of the discharge standard temperature, the number of discharges C2 when the discharge temperature is lower than the lower limit of the discharge standard temperature, the number of discharges C3 when the discharge exceeds the second user-set threshold, the number of discharges C4 when the SoC is less than the third user-set threshold at the end of the discharge, and the total number of discharges C all ;

[0221] Based on the third screening data, high temperature discharge information D1, low temperature discharge information D2, discharge depth information D3, and over discharge information D4 are determined, including: the number of discharges C1 based on the discharge temperature being higher than the upper limit of the discharge standard temperature, the total number of discharges C all Determine the high temperature discharge information D1, specifically:

[0222] D1=(1-C1 / C all )*100

[0223] Based on the discharge number C2 where the discharge temperature is lower than the lower limit of the discharge standard temperature, the total discharge number C all Determine the low temperature discharge information D2, specifically:

[0224] D2=(1-C2 / C all )*100

[0225] The number of discharges C3 and the total number of discharges C3 based on the discharge depth of the discharge process exceeding the second user-set threshold all Determine the discharge depth information D3, specifically:

[0226] D3=(1-C3 / C all )*100

[0227] Based on the discharge number C4 and the total discharge number C4 of the SoC being less than the third user-set threshold value at the end of the discharge, all Determine the over-discharge information D4, specifically:

[0228] D4=(1-C4 / C all )*100

[0229] The high-temperature discharge information D1, the low-temperature discharge information D2, the discharge depth information D3, and the over-discharge information D4 are weighted by a tomographic analysis method to obtain discharge behavior information.

[0230] Optionally, the analysis module 20 includes:

[0231] Based on the static data of the energy storage battery and the dynamic data of the energy storage battery, determine the median value of the voltage of all the battery cells in each frame of data and the median value of the temperature of all the battery cells in each frame of data;

[0232] Performing Euclidean distance calculation on the voltage of each battery cell during the charging process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the charging process;

[0233] Performing Euclidean distance calculation on the temperature of each battery cell during the charging process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the charging process;

[0234] Performing Euclidean distance calculation on the voltage of each battery cell during the discharge process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the discharge process;

[0235] Performing Euclidean distance calculation on the temperature of each battery cell during the discharge process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the discharge process;

[0236] Determine the data density value of the charging process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the charging process and an average Euclidean distance value of the temperature of each battery cell in the charging process;

[0237] Determine the data density value of the discharge process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the discharge process and an average Euclidean distance value of the temperature of each battery cell in the discharge process;

[0238] Determine the LOF value of each battery cell charging process based on the data density value of each battery cell charging process, and use the LOF value of each battery cell charging process to respectively determine the voltage consistency information of the charging process and the temperature consistency information of the charging process;

[0239] Determine the LOF value of each battery cell discharge process based on the data density value of each battery cell discharge process, and use the LOF value of each battery cell discharge process to respectively determine the voltage consistency information of the discharge process and the temperature consistency information of the discharge process;

[0240] The consistency information is obtained by weighting the voltage consistency information of the charging process, the temperature consistency information of the charging process, the voltage consistency information of the discharging process, and the temperature consistency information of the discharging process through a tomographic analysis method.

[0241] Optionally, the analysis module 20 includes:

[0242] Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain the number of occurrences of fault level L, the deduction value of fault level L, and the influence coefficient of duration of fault level L;

[0243] The fault information is determined based on the number of occurrences of the fault level L, the deduction value of the fault level L, and the influence coefficient of the duration of the fault level L, specifically:

[0244]

[0245] Where D is the fault information, N is the fault level, and n L is the number of occurrences of fault level L, P(L) is the deduction value of fault level L, f α(L) is the influence coefficient of fault level L duration.

[0246] Optionally, the weighting module 30 includes:

[0247] The energy storage battery comprehensive status information HLB is obtained by weighting the battery health information SoH Score, the charging behavior information ChargeScore, the discharging behavior information Discharge Score, the consistency information Consistency Score, and the fault information Fault Score through the entropy weight method. Specifically: HLB = ω1*SoH Score+ω2*Charge Score+ω3*Discharge Score+ω4*Consistency Score+ω5*Fault Score

[0248] Among them, ω 1、 ω 2、 ω 3、 ω 4、 ω5 are all weight values;

[0249] The comprehensive state level of the energy storage battery is determined based on the comprehensive state information of the energy storage battery.

[0250] The functions of the device in the embodiment of the present invention have been described in the above method embodiment, so for details not provided in the description of this embodiment, please refer to the relevant description in the above embodiment, and no further description will be given here.

[0251] Based on the same inventive concept, an embodiment of this specification also provides an electronic device.

[0252] The following describes an electronic device embodiment of the present invention, which can be regarded as a specific physical implementation of the method and device embodiments of the present invention. The details described in the electronic device embodiment of the present invention should be regarded as a supplement to the above method or device embodiments; details not disclosed in the electronic device embodiment of the present invention can be implemented with reference to the above method or device embodiments.

[0253] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. Figure 3 The electronic device 300 according to the embodiment of the present invention is described. Figure 3 The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0254] like Figure 3 As shown, the electronic device 300 is in the form of a general computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310), a display unit 340, etc.

[0255] The storage unit stores program codes, which can be executed by the processing unit 310, so that the processing unit 310 performs the steps according to various exemplary embodiments of the present invention described in the above processing method section of this specification. For example, the processing unit 310 can perform the following steps: Figure 1 Steps shown.

[0256] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache memory unit 3202 , and may further include a read-only memory unit (ROM) 3203 .

[0257] The storage unit 320 may also include a program / utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0258] Bus 330 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0259] The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable viewers to interact with the electronic device 300, and / or any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 350. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 360. The network adapter 360 may communicate with other modules of the electronic device 300 through the bus 330. It should be understood that although Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0260] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation method of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.) or on a network, including a number of instructions to enable a computing device (which can be a personal computer, server, or network device, etc.) to execute the above method according to the present invention. When the computer program is executed by a data processing device, the computer-readable medium can implement the above method of the present invention, that is: Figure 1 The method shown.

[0261] Figure 4 A schematic diagram of a computer-readable medium provided in accordance with an embodiment of the present specification.

[0262] accomplish Figure 1The computer program of the method shown can be stored on one or more computer readable media. The computer readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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.

[0263] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0264] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the viewer computing device, partially on the viewer device, as a stand-alone software package, partially on the viewer computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the viewer computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0265] In summary, the present invention can be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that general data processing devices such as microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0266] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0267] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0268] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for evaluating the comprehensive state of an energy storage battery, characterized in that: include: Obtain static data and dynamic data of energy storage batteries; Determine battery health information, charging behavior information, discharging behavior information, consistency information, and fault information based on the energy storage battery static data and the energy storage battery dynamic data; Obtaining a comprehensive state level of the energy storage battery based on the battery health information, the charging behavior information, the discharging behavior information, the consistency information, and the fault information; Based on the comprehensive status level of the energy storage battery, an energy storage battery adjustment strategy is generated.

2. The method for evaluating the comprehensive state of an energy storage battery according to claim 1, characterized in that: The determining of battery health information based on the energy storage battery static data and the energy storage battery dynamic data includes: Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain a SoC value, and determine the SoC interval corresponding to the current energy storage battery based on the SoC value; Filter the static data of the energy storage battery and the dynamic data of the energy storage battery based on the SoC interval corresponding to the current energy storage battery to obtain first filtered data; A SoH index is determined based on the first screening data, and the battery health information is obtained by fitting the SoH index mapping using a polynomial.

3. The method for evaluating the comprehensive state of an energy storage battery according to claim 2, characterized in that: The determining charging behavior information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes: The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the first preset key field to obtain the second screening data; the second screening data includes the number of charging times A1 when the charging temperature is higher than the upper limit value of the charging standard temperature, the number of charging times A2 when the charging temperature is lower than the lower limit value of the charging standard temperature, the number of charging times A3 when the charging current is greater than 0.5C, the number of charging times A4 when the charging end SoC is greater than the first user-set threshold, and the total number of charging times A5. all ; Based on the second screening data, high temperature charging information, low temperature charging information, fast charging information, and overcharge charging information are determined, including: the number of charging times A1 based on the charging temperature being higher than the upper limit of the charging standard temperature, the total number of charging times A all Determine the high temperature charging information B1, specifically: B1=(1-A1 / A all )*100 The number of charging times A2 based on the charging temperature being lower than the lower limit of the charging standard temperature, the total number of charging times A all Determine the low temperature charging information B2, specifically: <h2 style=";text-align:left;direction:ltr">B2=(1-A2 / A<h2 style=";text-align:left;direction:ltr"> all <h2 style=";text-align:left;direction:ltr"> )*100 Based on the charging current being greater than 0.5C, the charging times A3 and the total charging times A all Determine the fast charging information B3, specifically: <h2 style=";text-align:left;direction:ltr">B3=(1-A3 / A<h2 style=";text-align:left;direction:ltr"> all <h2 style=";text-align:left;direction:ltr"> )*100 Based on the charging end SoC being greater than the charging number A4 of the user-set threshold, the total charging number A all Determine the overcharge information B4, specifically: B4=(1-A4 / A all )*100 The high temperature charging information, the low temperature charging information, the fast charging information, and the overcharge charging information are weighted by a tomographic analysis method to obtain charging behavior information.

4. The method for evaluating the comprehensive state of an energy storage battery according to claim 3, characterized in that: The determining the discharge behavior information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes: The static data of the energy storage battery and the dynamic data of the energy storage battery are screened based on the second preset key field to obtain third screening data; the third screening data includes the number of discharges C1 when the discharge temperature is higher than the upper limit of the discharge standard temperature, the number of discharges C2 when the discharge temperature is lower than the lower limit of the discharge standard temperature, the number of discharges C3 when the discharge depth exceeds the second user-set threshold value during the discharge process, the number of discharges C4 when the SoC is less than the third user-set threshold value at the end of the discharge, and the total number of discharges C all ; Based on the third screening data, high temperature discharge information D1, low temperature discharge information D2, discharge depth information D3, and over discharge information D4 are determined, including: the number of discharges C1 based on the discharge temperature being higher than the upper limit of the discharge standard temperature, the total number of discharges C all Determine the high temperature discharge information D1, specifically: D1=(1-C1 / C all )*100 Based on the discharge number C2 where the discharge temperature is lower than the lower limit of the discharge standard temperature, the total discharge number C all Determine the low temperature discharge information D2, specifically: D2=(1-C2 / C all )*100 The number of discharges C3 and the total number of discharges C3 based on the discharge depth of the discharge process exceeding the second user-set threshold all Determine the discharge depth information D3, specifically: D3=(1-C3 / C all )*100 Based on the discharge number C4 and the total discharge number C4 of the SoC being less than the third user-set threshold at the end of the discharge, all Determine the over-discharge information D4, specifically: D4=(1-C4 / C all )*100 The high-temperature discharge information D1, the low-temperature discharge information D2, the discharge depth information D3, and the over-discharge information D4 are weighted by a tomographic analysis method to obtain discharge behavior information.

5. The method for evaluating the comprehensive state of an energy storage battery according to claim 4, characterized in that: The determining the consistency information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes: Based on the static data of the energy storage battery and the dynamic data of the energy storage battery, determine the median value of the voltage of all the battery cells in each frame of data and the median value of the temperature of all the battery cells in each frame of data; Performing Euclidean distance calculation on the voltage of each battery cell during the charging process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the charging process; Performing Euclidean distance calculation on the temperature of each battery cell during the charging process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the charging process; Performing Euclidean distance calculation on the voltage of each battery cell during the discharge process and the median voltage values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the voltage of each battery cell during the discharge process; Performing Euclidean distance calculation on the temperature of each battery cell during the discharge process and the median temperature values ​​of all battery cells in each frame of data to obtain an average Euclidean distance value of the temperature of each battery cell during the discharge process; Determine the data density value of the charging process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the charging process and an average Euclidean distance value of the temperature of each battery cell in the charging process; Determine the data density value of the discharge process of each battery cell by using a local interest group factor algorithm combined with an average Euclidean distance value of the voltage of each battery cell in the discharge process and an average Euclidean distance value of the temperature of each battery cell in the discharge process; Determine the LOF value of each battery cell charging process based on the data density value of each battery cell charging process, and use the LOF value of each battery cell charging process to respectively determine the voltage consistency information of the charging process and the temperature consistency information of the charging process; Determine the LOF value of each battery cell discharge process based on the data density value of each battery cell discharge process, and use the LOF value of each battery cell discharge process to respectively determine the voltage consistency information of the discharge process and the temperature consistency information of the discharge process; The consistency information is obtained by weighting the voltage consistency information of the charging process, the temperature consistency information of the charging process, the voltage consistency information of the discharging process, and the temperature consistency information of the discharging process through a tomographic analysis method.

6. The method for evaluating the comprehensive state of an energy storage battery according to claim 5, characterized in that: The determining the fault information based on the static data of the energy storage battery and the dynamic data of the energy storage battery includes: Analyze the static data of the energy storage battery and the dynamic data of the energy storage battery to obtain the number of occurrences of fault level L, the deduction value of fault level L, and the influence coefficient of duration of fault level L; The fault information is determined based on the number of occurrences of the fault level L, the deduction value of the fault level L, and the influence coefficient of the duration of the fault level L, specifically: Where D is the fault information, N is the fault level, and n L is the number of occurrences of fault level L, P(L) is the deduction value of fault level L, f α(L) is the influence coefficient of fault level L duration.

7. The method for evaluating the comprehensive state of an energy storage battery according to claim 6, characterized in that: The comprehensive state level of the energy storage battery is obtained by weighting the battery health information, the charging behavior information, the discharging behavior information, the consistency information, and the fault information, including: The energy storage battery comprehensive status information HLB is obtained by weighting the battery health information SoH Score, the charging behavior information ChargeScore, the discharging behavior information Discharge Score, the consistency information Consistency Score, and the fault information Fault Score through the entropy weight method. Specifically: HLB = ω1*SoH Score+ω2*Charge Score+ω3*Discharge Score+ω4*Consistency Score+ω5*Fault Score Among them, ω 1、 ω 2、 ω 3、 ω 4、 ω5 are all weight values; The comprehensive state level of the energy storage battery is determined based on the comprehensive state information of the energy storage battery.

8. A device for evaluating the comprehensive state of an energy storage battery, characterized in that: include: An acquisition module is used to acquire static data and dynamic data of energy storage batteries; An analysis module, used to determine battery health information, charging behavior information, discharging behavior information, consistency information, and fault information based on the energy storage battery static data and the energy storage battery dynamic data; A weighting module, used for obtaining a comprehensive state level of the energy storage battery by weighting the battery health information, the charging behavior information, the discharging behavior information, the consistency information, and the fault information; A generation module is used to generate an energy storage battery adjustment strategy based on the comprehensive status level of the energy storage battery.

9. An electronic device, wherein: The electronic device includes: processor; and, A memory storing computer executable instructions which, when executed, cause the processor to perform a method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method of any one of claims 1 to 7 is implemented.

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