CN111707957B - Method and device for estimating residual value of battery of electric vehicle - Google Patents

Method and device for estimating residual value of battery of electric vehicle Download PDF

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CN111707957B
CN111707957B CN202010328486.7A CN202010328486A CN111707957B CN 111707957 B CN111707957 B CN 111707957B CN 202010328486 A CN202010328486 A CN 202010328486A CN 111707957 B CN111707957 B CN 111707957B
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battery
index
residual value
evaluated
data
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CN111707957A (en
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杨正球
彭伟
刘辰
修佳鹏
常剑多
王安生
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Beijing University of Posts and Telecommunications
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M3/00—Investigating fluid-tightness of structures
    • G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/4285—Testing apparatus
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Abstract

The invention provides a method and a device for estimating battery residual value of an electric vehicle, wherein the method generates residual value characteristic indexes capable of expressing the performance of each aspect of a battery pack to be estimated based on basic data of a battery, utilizes the residual value characteristic indexes to comprehensively evaluate the battery residual value from multiple aspects, and adopts a weighting and product-solving mode to amplify the influence of each residual value characteristic index on the battery residual value so as to obtain an evaluation result with higher accuracy and closer to a real value. Furthermore, the weight coefficient of each residual value characteristic index is obtained through training in a computer learning mode based on the sample training set, so that the evaluation effect of the battery residual value is closer to the true value, and compared with the weight coefficient determined through a theoretical analysis method or an expert evaluation method, the influence of artificial subjective factors can be greatly reduced.

Description

Method and device for estimating residual value of battery of electric vehicle
Technical Field
The invention relates to the technical field of battery performance diagnosis, in particular to a method and a device for estimating a residual value of a battery of an electric vehicle.
Background
In recent years, the global electric automobile market is rapidly increasing, the domestic electric automobile sales volume is obviously improved, and a large number of lithium ion power batteries are necessarily retired due to capacity attenuation in the coming years. The capacity of the power battery is generally 70% -80% of the rated capacity during retirement, and although the power battery cannot meet the use requirements of automobiles, the power battery can still store certain energy and can be secondarily utilized (namely, gradient utilization) in other fields to be used as a carrier of electric energy. For example, the method is applied to the aspects of low-speed vehicles, electric tricycles, electric motorcycles, charging station energy storage, peak clipping and valley filling of thermal power stations or photovoltaic and the like, and the economic benefit is improved. Therefore, it is necessary to estimate the remaining value (residual value) of such a power battery. However, at present, domestic research on the method is not much, and an efficient evaluation method is not available.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for estimating a residual value of a battery of an electric vehicle, which have solved a problem in the prior art that it is difficult to effectively estimate a residual value of a power battery.
The technical scheme of the invention is as follows:
in one aspect, the invention provides a method for estimating a residual value of a battery of an electric vehicle, comprising the following steps:
acquiring basic data of a battery pack to be evaluated, wherein the basic data comprises basic parameters of the battery pack to be evaluated and data generated by detecting the battery pack to be evaluated by a battery management system in a driving process;
calculating a residual value characteristic index of the battery pack to be evaluated according to the basic data, wherein the residual value characteristic index comprises the following steps: the index of health degree, the index of residual service life, the index of consistency of monocell and the index of safety, and one or more of the index of discharge capacity, the index of charge capacity and the index of heating level;
and adding a weight coefficient to each residual value characteristic index to be used as an exponent to perform power operation, and then performing quadrature to obtain the battery residual value.
In some embodiments, the battery residual value is obtained by performing weighted integration on each residual value characteristic index, and the calculation formula is as follows:
Figure BDA0002464093040000021
wherein RV is a battery residual value, SOH is a health degree index, RUL is a residual service life index, Co is a single battery consistency index, S is a safety index, Dc is a discharge capacity index, Ch is a charging capacity index, and H is a heating level indexMarking; omega1、ω2、ω3……ω7The weight coefficients are respectively a health degree index, a residual service life index, a single cell consistency index, a safety index, a discharging capacity index, a charging capacity index and a heating level index, and are positive and real numbers; n is a positive number greater than 1; RV, SOH, RUL, Co, S, Dc, Ch and H have values of [0, 1%]Within the interval and positively correlated to battery performance.
In some embodiments, the step of obtaining the security index comprises:
acquiring a plurality of set evaluation parameters in the basic data of the battery pack to be evaluated, wherein the set evaluation parameters comprise: insulation detection results, air tightness detection results, cell bulging detection results and cell leakage detection results;
acquiring the set evaluation parameters of a plurality of pre-collected sample battery packs and corresponding safety indexes, wherein the safety indexes of each sample battery pack are set into scores from low to high according to safety as a first safety index value, a second safety index value and a third safety index value;
and screening out the set number of sample battery packs closest to the battery pack to be evaluated by adopting a proximity algorithm based on the set evaluation parameters, and determining the scores of most of the sample battery packs as the safety indexes of the battery pack to be evaluated.
In some embodiments, the base data comprises: initial internal resistance R of battery pack to be evaluatednewInternal resistance at end of life REOLThe internal resistance R in the current state; rated number of charges C of each sample battery packallCurrent number of charges Cnow(ii) a Voltage V of the nth single cell constituting the battery pack to be evaluated during operationnAnd the mean value of the voltage
Figure BDA0002464093040000027
Current discharge power Dp of battery pack to be evaluatednowRated discharge power DpratedCurrent charging power CpnowRated charging power CpratedHeating power Hp at factory shipmentnewCurrent heating power Hpnow；
Based on the basic data, the calculation formula of each residual value characteristic index is as follows:
Figure BDA0002464093040000022
Figure BDA0002464093040000023
Figure BDA0002464093040000024
wherein Q is a constant, and delta is the standard deviation of the voltage of the single batteries forming the battery pack to be evaluated;
Figure BDA0002464093040000025
Figure BDA0002464093040000026
Figure BDA0002464093040000031
in some embodiments, the weight coefficient ω1、ω2、ω3……ω7Obtained by means of machine learning, including:
acquiring basic data of a plurality of sample battery packs, wherein the basic data comprises basic parameters of the sample battery packs and data generated by a battery management system in the driving process;
calculating residual value characteristic indexes corresponding to the sample battery packs according to the basic data to serve as input, adding battery residual values corresponding to the sample battery packs to serve as label output, and forming a sample data set; wherein the residual characteristic index includes: the index of health degree, the index of residual service life, the index of consistency of monocell and the index of safety, and one or more of the index of discharge capacity, the index of charge capacity and the index of heating level;
training an initial model by using the sample data set to obtain a battery residual value evaluation model; wherein the initial model is
Figure BDA0002464093040000032
Extracting the weight coefficient in the battery residual value evaluation model as corresponding omega1、ω3、ω3……ω7。
In some embodiments, after the forming the sample data set, further comprising:
randomly sampling the sample data set according to a set proportion to obtain a training set, a verification set and a test set;
training an initial model by adopting the training set to obtain a battery residual value evaluation model; wherein the initial model is
Figure BDA0002464093040000033
Figure BDA0002464093040000034
The verification set is adopted to carry out effect verification on the battery residual value evaluation model and carry out parameter tuning, and the weight coefficient in the tuned battery residual value evaluation model is extracted as the corresponding omega1、ω2、ω3……ω7；
And testing the optimized battery residual value evaluation model by adopting the test set.
In some embodiments, testing the tuned battery residual evaluation model using the test set includes:
inputting the test set into the optimized battery residual value evaluation model, and calculating the root mean square error of the evaluation result, wherein the value of the root mean square error is inversely related to the accuracy of the battery residual value evaluation model.
In some embodiments, the weight coefficients in the battery residual evaluation model are extracted as corresponding ω1、ω2、ω3……ω7The method also comprises the following steps:
training a plurality of battery residual value evaluation models, averaging the corresponding weight coefficients in each battery residual value evaluation model to obtain a fused weight coefficient, and generating the fused battery residual value evaluation model;
taking the weight coefficient in the fused battery residual value evaluation model as corresponding omega1、ω2、ω3……ω7。
In some embodiments, before obtaining the basic data of the battery pack to be evaluated, the method further includes:
inquiring basic parameters of the battery packs of various types of electric vehicles from a battery manufacturer or crawling the basic parameters from the Internet;
acquiring running data generated in the running process of a plurality of battery packs to be evaluated from a vehicle-enterprise monitoring platform or a national monitoring platform;
and storing the basic parameters of the battery packs of the electric automobiles of various models and the running data of the battery packs to be evaluated into a relational database or a big data storage system for direct acquisition during the estimation of the residual value of the battery.
In another aspect, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements
The method has the advantages that residual value characteristic indexes capable of expressing various characteristics of the battery pack to be evaluated are derived through basic data, comprehensive evaluation is conducted on the battery residual value from multiple aspects by utilizing the residual value characteristic indexes, the influence of each residual value characteristic index on the battery residual value can be amplified by adopting a weighting and product-solving mode, and the evaluation result with higher accuracy and closer to the true value is obtained.
Further, the weight coefficient corresponding to each residual value characteristic index is obtained through a computer learning mode based on the sample training set, so that the evaluation accuracy is higher.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present invention are not limited to the specific details set forth above, and that these and other objects that can be achieved with the present invention will be more clearly understood from the detailed description that follows.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. For purposes of illustrating and describing some portions of the present invention, corresponding parts of the drawings may be exaggerated, i.e., may be larger, relative to other components in an exemplary apparatus actually manufactured according to the present invention. In the drawings:
FIG. 1 is a schematic flow chart illustrating a method for estimating a residual battery capacity of an electric vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a security index obtaining process in the method for estimating a residual battery value of an electric vehicle according to an embodiment of the invention;
FIG. 3 is a schematic flow chart illustrating a process of obtaining a weight coefficient in the method for estimating a residual battery value of an electric vehicle according to an embodiment of the invention;
FIG. 4 is a schematic flow chart illustrating a process of obtaining a weight coefficient in a method for estimating a residual battery value of an electric vehicle according to another embodiment of the present invention;
FIG. 5 is a diagram illustrating a data mapping relationship in a method for estimating a residual battery capacity of an electric vehicle according to an embodiment of the present invention;
fig. 6 is a flowchart illustrating a method for obtaining a weight coefficient by machine learning in an estimation method of a battery residual value of an electric vehicle according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following embodiments and accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and/or processing steps closely related to the scheme according to the present invention are shown in the drawings, and other details not so relevant to the present invention are omitted.
It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
It is also noted herein that the term "coupled," if not specifically stated, may refer herein to not only a direct connection, but also an indirect connection in which an intermediate is present.
After the market of electric automobiles is undergoing rapid development, a large amount of power batteries are aged and retired. The power battery is generally 70-80% of rated capacity in the retirement process, and can be applied to other fields in a gradient manner to ensure reasonable and repeated utilization of resources. Because the states of the automobile power battery during retirement are different, in order to meet the requirements in the subsequent secondary utilization process, the automobile power battery is required to evaluate the battery residue value during retirement so as to be adaptively applied to different recycling fields. In the prior art, the evaluation of the battery residual value is one-sided, the mode and the method are single, and the actual value cannot be accurately approached.
Therefore, the invention provides a method and a device for estimating the battery residual value of an electric vehicle, which are used for evaluating the battery residual value of the electric vehicle by combining the residual value characteristic indexes of a plurality of angles so as to obtain the battery residual value evaluation result closest to the true value.
The method for estimating the residual value of the battery of the electric automobile, as shown in fig. 1, comprises the following steps of S101-S103:
step S101: and acquiring basic data of the battery pack to be evaluated, wherein the basic data comprises basic parameters of the battery pack to be evaluated and data generated by detecting the battery pack to be evaluated by a battery management system in the driving process.
Step S102: calculating a residual value characteristic index of the battery pack to be evaluated according to the basic data, wherein the residual value characteristic index comprises the following steps: the index of health degree, the index of residual service life, the index of consistency of monocell and the index of safety, and one or more of the index of discharge capacity, the index of charge capacity and the index of heating level.
Step S103: and adding a weight coefficient to each residual value characteristic index to be used as an exponent to perform power operation, and then performing quadrature to obtain the battery residual value.
In step S101, the basic data of the battery pack to be evaluated includes two aspects, on one hand, basic parameters related to the model of the battery pack to be evaluated, which are determined when the battery pack to be evaluated is shipped from a factory; on the other hand, the BATTERY management system BMS (BATTERY MANAGEMENT SYSTEM) collects the driving data of the BATTERY pack to be evaluated while the vehicle is driving. The basic parameters may include a rated capacity, a rated power, a rated charge-discharge frequency, a rated mileage, an initial internal resistance, and the like of the battery pack to be evaluated. The travel data may include: total current, total voltage, cell voltage, state of charge, temperature, mileage, operating duration, alarm data, fault data, and the like.
In some embodiments, before step S101, that is, before acquiring the basic data of the battery pack to be evaluated, the method further includes S1011 to S1013:
s1011: and inquiring basic parameters of the battery pack of each type of electric automobile from a battery manufacturer or crawling the basic parameters from the Internet.
S1012: and acquiring running data generated in the running process of the plurality of battery packs to be evaluated from the vehicle-enterprise monitoring platform or the national monitoring platform.
S1013: and storing the basic parameters of the battery packs of the electric automobiles of various models and the running data of the battery packs to be evaluated into a relational database or a big data storage system for direct acquisition during the estimation of the residual value of the battery.
In order to obtain the basic information of the battery pack to be evaluated at any time, the data acquisition work needs to run through the whole battery production and use process. In this embodiment, S1011 obtains the basic parameters of the electric vehicle battery packs of various models by querying from a battery manufacturer or crawling on the internet.
In order to effectively acquire the driving data in the evaluation process of the battery residual value, in this embodiment S1012, in the use process of the battery to be evaluated, the driving data generated in the driving process of the plurality of monitored battery packs of the electric vehicle is acquired through the monitoring way of the vehicle-enterprise monitoring platform or the national monitoring platform.
And S1013, storing the basic parameters of the electric automobile battery packs of different models and the driving data of the monitored electric automobile battery packs acquired by each vehicle enterprise monitoring platform or national monitoring platform in a relational database or a big data storage system for calling and using at any time. In step S102, a secondary evaluation index, i.e., a residual value feature index, is calculated and acquired based on the basic data acquired in step S101. The residual value characteristic indexes are generated from multiple angles, so that the battery residual value of the battery pack to be evaluated is comprehensively, completely and accurately evaluated.
Specifically, in the residual characteristic index, a Health index (SOH, State Of Health) is a ratio Of an actual capacity to a nominal capacity Of the battery pack to be evaluated, and may be measured by a resistance-folding algorithm, or may be calculated by detecting a working condition Of a charging or discharging process to obtain the Health index. The health index is calibrated in percentage.
The remaining service life indicator (RUL) is a calibration of the remaining usable time or number of times of the battery pack to be evaluated, and is usually calibrated by using the number of charge and discharge cycles, and in some embodiments, may also be calibrated by using the battery use time.
The single cell consistency index (Co) is used for evaluating the performance difference of a plurality of single cells forming the battery pack to be evaluated, the performance difference of the single cells can directly influence the energy density, the safety and the durability of the battery to be evaluated, and the battery pack to be evaluated has better performance when the consistency is higher. Specifically, the cell consistency index may be obtained by comparing voltage values, temperatures, internal resistances, or other parameters of the plurality of cells.
The safety index (S) is used for evaluating the use reliability of the battery pack to be evaluated, specifically, the safety index (S) can be used for carrying out comparison of various indexes based on the existing data, and the safety index of the existing data closest to the battery pack to be evaluated is used as the safety index of the battery pack.
The discharge capacity index (Dc), the charge capacity index (Ch) and the heat generation level index (H) are used for evaluating the discharge level and the heat generation level of the battery pack to be evaluated in the process of operating the charge level. The discharge capacity index and the charge capacity index can be calibrated by adopting charge and discharge power, and in some embodiments, the discharge capacity can also be calibrated by adopting parameters such as discharge multiplying power, hour rate and the like under specified conditions. The heat level indicator may be calibrated using average temperature, heat power, or other parameters that reflect the temperature level.
In step S103, the residual characteristic indicators generated according to the basic data are mapped to the battery residual, and in combination with the evaluation requirement of the battery residual, since any performance of the battery is low, which may result in that the battery pack cannot be effectively utilized, in this embodiment, in order to improve the influence of each residual characteristic indicator on the final battery residual, the battery residual is calculated by taking each residual characteristic indicator as a product, and simultaneously, in order to embody and distinguish the importance degree of each residual characteristic indicator, a weighting coefficient is taken as an index to perform an exponentiation operation on the residual characteristic indicators.
In some embodiments, the battery residual value is obtained by performing weighted integration on each residual value characteristic index, and the calculation formula is as follows:
Figure BDA0002464093040000071
wherein RV is a battery residual value, SOH is a health degree index, RUL is a residual service life index, Co is a single battery consistency index, S is a safety index, Dc is a discharge capacity index, Ch is a charge capacity index, and H is a heating level index; omega1、ω2、ω3……ω7The weight coefficients are respectively a health degree index, a residual service life index, a single cell consistency index, a safety index, a discharging capacity index, a charging capacity index and a heating level index, and are positive and real numbers; n is a positive number greater than 1; RV, SOH, RUL, Co, S, Dc, Ch and H have values of [0, 1%]Within the interval and positively correlated to battery performance.
In this embodiment, the value of each residual characteristic index is within the [0,1] interval, and the influence of each residual characteristic index on the battery residual is amplified by means of multiplication, and on this basis, in order to ensure the effect of the weight coefficient, the inverse of the weight coefficient is used as an exponent to perform power operation on the residual characteristic index, and then the product is obtained. In the embodiment, the reciprocal of the residual value characteristic index number N is used as an exponent, the product is subjected to power operation, and the product is remapped to a [0,1] interval, so as to improve the evaluation effect of the battery residual value RV.
Each residual value characteristic index is generated based on basic data and can be obtained by adopting different methods for operation or evaluation. Weight coefficient omega1、ω2、ω3……ω7The method can be determined by a theoretical analysis method or an expert evaluation method, and can also be obtained by evaluating by an entropy method based on the existing data.
In some embodiments, as shown in fig. 2, the step of acquiring the security index includes steps S201 to S203:
step S201: acquiring a plurality of set evaluation parameters from basic data of a battery pack to be evaluated, wherein the set evaluation parameters comprise: insulation detection results, air tightness detection results, cell bulging detection results and cell leakage detection results;
step S202: acquiring preset evaluation parameters and corresponding safety indexes of a plurality of pre-collected sample battery packs, wherein the safety indexes set scores from low to high according to safety as a first safety index value, a second safety index value and a third safety index value;
step S203: and screening out a set number of sample battery packs closest to the battery pack to be evaluated by adopting a proximity algorithm based on the set evaluation parameters, and determining the scores of most of the sample battery packs as the safety indexes of the battery pack to be evaluated.
Under the same evaluation standard, two battery packs with similar states should have similar safety indexes, in this embodiment, state data of a large number of sample battery packs and corresponding safety indexes are collected for comparison; and screening out a sample battery pack with the state similar to that of the battery pack to be evaluated in an approximation degree comparison mode, and endowing the battery pack to be evaluated with a corresponding safety index.
Specifically, in step S201, in order to reflect the state of the battery pack to be evaluated as needed, set evaluation parameters are selected and determined, and the types of the set evaluation parameters should reflect the safety performance of the battery.
In step S202, the set evaluation parameters and the safety indexes corresponding to the plurality of sample battery packs in the existing data are obtained as a comparison database. Wherein the safety indexes are classified according to grades, and s is set according to the safety performance from low to high1、s2And s3Three scores, should belong to the (0, 1) interval. In some embodiments, the scoring scores of multiple levels may also be set according to actual needs. The order of step S201 and step S202 is not fixed, and may be performed simultaneously.
In step S203, a set evaluation parameter of the battery pack to be evaluated and each sample battery pack is compared by using a proximity algorithm (KNN, k-nearest neighbor), and a set number of sample battery packs most similar to the state of the battery pack to be evaluated are screened out. The set number is preferably an odd number, generally not more than 20, and the scores of most of the safety indexes are assigned to the battery pack to be evaluated, so that the accuracy is high.
Illustratively, insulation detection results, air tightness detection results, cell bulging detection results and cell leakage detection results of a plurality of sample battery packs are obtained, and meanwhile safety indexes of the sample battery packs are recorded as existing data. Acquiring an insulation detection result, an air tightness detection result, a cell bulging detection result and a cell leakage detection result of the battery pack to be evaluated; and screening out 9 sample battery packs closest to each detection result of the battery pack to be evaluated in the existing data by adopting a proximity algorithm (KNN, k-nearest neighbor), and taking the safety indexes of most of the 9 sample battery packs as the safety indexes of the battery pack to be evaluated.
In some embodiments, the base data includes: initial internal resistance R of each sample battery packnewInternal resistance at end of life REOLThe internal resistance R in the current state; rated number of charges C of each sample battery packa11Current number of charges Cnow(ii) a Voltage V of the nth single cell constituting the battery pack to be evaluated during operationnAnd the mean value of the voltage
Figure BDA0002464093040000097
Current discharge power Dp of each sample battery packnowRated discharge power DpratedCurrent charging power CpnowRated charging power CpratedHeating power Hp at factory shipmentnewCurrent heating power Hpnow；
Based on the basic data, the calculation formula of each residual value characteristic index is as follows:
Figure BDA0002464093040000091
Figure BDA0002464093040000092
Co＝Q3δ..................(4)
Figure BDA0002464093040000093
wherein Q is a constant, and delta is the standard deviation of the voltage of the single batteries forming the battery pack to be evaluated;
Figure BDA0002464093040000094
Figure BDA0002464093040000095
Figure BDA0002464093040000096
in the embodiment of the invention, each residual value characteristic index is calculated based on selected basic data, wherein the health index SOH is evaluated by adopting an internal folding algorithm. The remaining service life index RUL was evaluated using the number of charge and discharge cycles. The single cell consistency index Co is converted by the standard deviation of the voltage of the single cell, wherein Q is a constant, and preferably, when the discharge rate is 1C, Q is 0.0185; when the discharge rate of the battery is 2C, Q is 0.02154; when the discharge rate of the battery is 3C, Q is 0.0267.
The discharge capacity index Dc is calibrated by adopting the ratio of the current discharge power to the rated discharge power; the charging capacity index Ch is calibrated by adopting the ratio of the current charging power to the rated charging power; and the heating level index H is calibrated by adopting the ratio of the heating power when the heating power leaves the factory to the current heating power.
Based on the method for obtaining the safety index S given in steps S201 to S203 and the method for calibrating the health index SOH, the remaining service life index RUL, the cell consistency index Co, the discharge capacity index Dc, the charge capacity index Ch, and the heat generation level index H in this embodiment, the larger the value of each residual value characteristic index is, the better the evaluation effect is in the corresponding value range.
It should be emphasized that the calculation method of each residual characteristic index is not limited to that provided in the present embodiment, and it should be understood that the calibration method that can be used for calibrating the corresponding characteristic and whose value range meets the requirement all belongs to the protection scope of the present invention.
Based on the calculation of the battery residual value, equation (1), in some embodiments, the weight factor ω1、ω2、ω3……ω7Obtained by means of machine learning, as shown in fig. 3, includes steps S301 to S304:
step S301: basic data of a plurality of sample battery packs are obtained, and the basic data comprise basic parameters of the sample battery packs and data generated by a battery management system during driving.
Step S302: calculating residual value characteristic indexes corresponding to the sample battery packs according to the basic data to serve as input, adding battery residual values corresponding to the sample battery packs to serve as label output, and forming a sample data set; wherein, the characteristic index of the residual value comprises: the index of health degree, the index of residual service life, the index of consistency of monocell and the index of safety, and one or more of the index of discharge capacity, the index of charge capacity and the index of heating level.
Step S303: training an initial model by using the sample data set to obtain a battery residual value evaluation model; wherein, the initial model is a calculation formula (1).
Step S304: extracting weight coefficients in the battery residual value evaluation model as corresponding omega1、ω2、ω3……ω7。
In this embodiment, in order to obtain a more generalized battery residual value calculation formula to improve the accuracy of evaluation, the initial model is trained based on the sample data set in a machine learning manner to obtain a better weight coefficient. Compared with a general expert evaluation method, the method can greatly reduce errors caused by human subjective factors.
Specifically, in step S301 and step S302, the collected basic data of each sample battery pack should ensure that the corresponding residual characteristic index is obtained through calculation or evaluation, and the specific calculation or evaluation manner is not limited, and it should be understood that a reasonable and effective mapping relationship between the basic data and the residual characteristic index is included. It is emphasized that the residual characteristic index of the sample battery pack to be obtained here must be consistent with the residual characteristic index used for evaluating the battery pack to be evaluated in step S102.
Preferably, in other embodiments, the content of the basic data of the sample battery pack collected in step S301 is kept consistent with that in step S101, and in step S302, the mapping relationship between the basic data of the sample battery pack and the residual characteristic index is consistent with that in step S102, so as to ensure that the machine learning result can be applied to steps S101 to S103, and therefore, higher accuracy and stronger generalization effect can be obtained.
In some embodiments, after step S301, that is, after obtaining the basic data of the plurality of sample battery packs, the method further includes:
and cleaning basic data of the sample battery pack, including data deduplication, useless data removal and missing data filling.
The cleaning of the data comprises data deduplication, useless data removal and missing data padding. Removing duplication, namely removing repeated data; removing useless data (if a piece of data is missing more fields, the data is regarded as useless data); missing data is filled, and the filling method can adopt an averaging method, namely, the average value of corresponding fields of the upper data and the lower data is taken as a missing value and the like.
Further, in steps S302 and S303, a sample data set is established using the residual characteristic index of the sample battery pack as input and the corresponding battery residual as an output tag. In order to ensure that the weight coefficients obtained by machine learning can be applied to the calculation formula (1), the calculation formula (1) is directly used as an initial model for training.
Specifically, the sample data set is input into the initial model and then trainedAnd fitting through algorithms such as linear regression, LSTM and GRU and the like to obtain a battery residual value evaluation model. The structure of the battery residual evaluation model is consistent with the calculation formula (1), and the weight coefficient can be used as the optimized omega obtained after machine learning1、ω2、ω3……ω7。
In some embodiments, in step S304, the weight coefficients in the battery residual evaluation model are extracted as corresponding ω1、ω2、ω3……ω7The method also comprises the following steps:
s3041: training a plurality of battery residual value evaluation models, averaging the corresponding weight coefficients in each battery residual value evaluation model to obtain a fused weight coefficient, and generating a fused battery residual value evaluation model;
s3042: taking the weight coefficient in the fused battery residual value evaluation model as corresponding omega1、ω2、ω3……ω7。
In the embodiment, a plurality of better battery residual value evaluation models are obtained through training and are fused to obtain a better evaluation effect. Specifically, the average value of the corresponding weight coefficients in each battery residual value evaluation model is used as the fused weight coefficient. In other embodiments, the value of the weight may also be assigned according to the effect of each battery residual evaluation model.
In some embodiments, after the step S302 and after the sample data set is formed, as shown in fig. 4, the method further includes steps S401 to S404:
step S401: and randomly sampling the sample data set according to a set proportion to obtain a training set, a verification set and a test set.
Step S402: training an initial model by adopting a training set to obtain a battery residual value evaluation model; wherein, the initial model adopts a calculation formula (1).
Step S403: effect verification is carried out on the battery residual value evaluation model by adopting a verification set, parameters are adjusted and optimized, and weight coefficients in the adjusted and optimized battery residual value evaluation model are extracted and used as corresponding omega1、ω2、ω3……ω7。
Step S404: and testing the optimized battery residual value evaluation model by adopting a test set.
In this embodiment, in step S401, in order to improve the generalization of machine learning, a sample data set is randomly sampled according to a set proportion to obtain a training set, a verification set, and a test set, for example, according to 8:1:1, dividing the ratio; here, the generalization of training can be further improved by adopting a random sampling mode.
In step S402, in order to obtain the optimal weight coefficient by fitting, a training set is input to an initial model for training, and the initial model is fitted by using algorithms such as linear regression, LSTM, GRU, and the like using the calculation formula (1) to obtain a battery residual evaluation model.
In step S403, the verification set is used to perform effect verification and parameter tuning on the battery residual evaluation model, where the parameter tuning may be performed by grid search and cross validation with ten folds. Using the weight coefficient in the adjusted battery residual value evaluation model as corresponding omega in the calculation formula (1)1、ω2、ω3……ω7。
In step S404, the optimized battery residual evaluation model is tested by using a test set to evaluate the accuracy thereof.
In some embodiments, in step S404, the testing the tuned battery residual evaluation model with the test set includes:
and inputting the test set into the optimized battery residual value evaluation model, and calculating the root mean square error of the evaluation result, wherein the value of the root mean square error is inversely related to the accuracy of the battery residual value evaluation model.
Specifically, the test set inputs the battery residual evaluation model to obtain the battery residual, and calculates the root mean square error by combining the output label marked in the test set, so as to measure the deviation between the observed value and the true value calculated by the battery residual evaluation model and reflect the effect of the battery residual evaluation model.
In another aspect, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method.
In another aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above method.
In some embodiments, as shown in fig. 5, the collection of the basic data of the battery pack to be evaluated may include basic information and driving data, wherein the basic information includes: rated capacity, rated power, rated charge-discharge times, rated travel mileage and initial internal resistance, wherein the travel data comprise: total current, total voltage, cell voltage, SOC (state of charge), temperature, mileage, operating duration, alarm data, fault data, and the like. Further, referring to the corresponding relationship in fig. 5, the secondary evaluation indexes SOH, RUL, Co, S, Dc, Ch, and H are converted from the basic data, and further, the residual value is estimated to obtain the battery residual value RV.
The comprehensiveness of the basic data has great influence on the calculation of the index, and the more comprehensive the basic data is, the more accurate the calculation of the index can be. In this embodiment, there are 2 paths for obtaining the basic information in the basic data: one is obtained by inquiring from a battery manufacturer according to the model of the battery; the other is obtained by crawling a webpage related to battery parameters in the Internet, such as a parameter page of a new energy automobile. The obtained basic information is classified and stored according to the battery model, the battery information of the same model can have a plurality of data sources, and the complete information is obtained by merging a plurality of data. The running data in the basic data can be acquired from the new energy automobile monitoring platform, general new energy automobile manufacturers can have own monitoring platforms, and the new energy automobile monitoring platforms are provided, so that the data reported in the running process of the new energy automobile can be reported to the monitoring platforms, and the running data of the related batteries can be downloaded from the monitoring platforms. In the two types of data, the data volume of the basic information of the battery is not large, and the basic information of the battery can be directly stored in a relational database; the amount of battery driving data is large and needs to be saved in a large data storage system.
In some embodiments, as shown in fig. 6, the weight coefficients in the calculation formula (1) are obtained by machine learning, first, basic data of the sample battery pack is collected, and data preprocessing is performed. The collected battery data are all raw data of the battery, including basic information of the battery and data (driving data) reported by the BMS in the driving process of the battery; and dividing the actual market value of each sample battery pack by the original value of the battery to obtain a battery residual value, and using the battery residual value as a label of the corresponding sample battery pack to form a sample data set. Preprocessing includes data cleansing and feature engineering. The cleaning of the data includes data deduplication, useless data removal (if a field of data is missing more, the field is regarded as useless data), missing data padding (the padding method can adopt an averaging method, that is, the average value of corresponding fields of upper and lower data is taken as a missing value), and the like. And after the data preprocessing is finished, performing characteristic engineering, and calculating or evaluating based on basic data to obtain 7 residual value characteristic indexes of SOH, RUL, Co, S, Dc, Ch and H. Model training then follows. After the characteristic index of the residual value is obtained, the sample data set is randomly sampled, and the data set is divided into a training set, a verification set and a test set according to the ratio of 8:1: 1. Then training an initial model (calculation formula (1)) by using a training set, and inputting data into machine learning and deep learning algorithms such as linear regression, LSTM and the like for learning to obtain a corresponding fusion model; verifying the fusion model by using a verification set, and performing parameter tuning by using algorithms such as grid search, ten-fold cross verification and the like, namely changing different parameters on the basis of the verification set to obtain a better result; the test set was used to examine the generalization ability of the model. And (3) evaluating the conditions of the models by using RMSE as an index, selecting the models with good effect, wherein the smaller the RMSE is, the better the model effect is.
In some embodiments, several effective models can be further fused to try to obtain a better model. The model fusion can adopt a mode of averaging a plurality of models to fuse the plurality of models into one model.
In summary, the method and the device for estimating the battery residual value of the electric vehicle generate the residual value characteristic indexes capable of expressing the performance of each aspect of the battery pack to be evaluated based on the basic data of the battery, make comprehensive evaluation on the battery residual value from multiple aspects by using the residual value characteristic indexes, and amplify the influence of each residual value characteristic index on the battery residual value by adopting a weighting and product-solving mode so as to obtain an evaluation result with higher accuracy and closer to a real value. Furthermore, the weight coefficient of each residual value characteristic index is obtained through training in a computer learning mode based on the sample training set, so that the evaluation effect of the battery residual value is closer to the true value, and compared with the weight coefficient determined through a theoretical analysis method or an expert evaluation method, the influence of artificial subjective factors can be greatly reduced.
Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein may be implemented as hardware, software, or combinations of both. Whether this is done in hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments in the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for estimating a residual value of a battery of an electric vehicle is characterized by comprising the following steps:
acquiring basic data of a battery pack to be evaluated, wherein the basic data comprises basic parameters of the battery pack to be evaluated and data generated by detecting the battery pack to be evaluated by a battery management system in a driving process;
calculating a residual value characteristic index of the battery pack to be evaluated according to the basic data, wherein the residual value characteristic index comprises the following steps: a health indicator, a remaining service life indicator, a single cell consistency indicator, and a safety indicator, and one or more of the following: a discharge capacity index, a charge capacity index, a heat level index;
and adding a weight coefficient to each residual value characteristic index to be used as an exponent to perform power operation, and then performing quadrature to obtain the battery residual value.
2. The method for estimating the battery residual value of the electric vehicle according to claim 1, wherein the battery residual value is obtained by performing weighted integration on each residual value characteristic index, and the calculation formula is as follows:
Figure FDA0002930603570000011
wherein RV is a battery residual value, SOH is a health degree index, RUL is a residual service life index, Co is a single battery consistency index, S is a safety index, Dc is a discharge capacity index, Ch is a charge capacity index, and H is a heating level index; omega1、ω2、ω3……ω7The weight coefficients are respectively a health degree index, a residual service life index, a single cell consistency index, a safety index, a discharging capacity index, a charging capacity index and a heating level index, and are positive and real numbers; n is a positive number greater than 1; RV, SOH, RUL, Co, S, Dc, Ch and H have values of [0, 1%]Within the interval and positively correlated to battery performance.
3. The method for estimating the battery residual value of the electric vehicle according to claim 1 or 2, wherein the step of obtaining the safety index includes:
acquiring a plurality of set evaluation parameters in the basic data of the battery pack to be evaluated, wherein the set evaluation parameters comprise: insulation detection results, air tightness detection results, cell bulging detection results and cell leakage detection results;
acquiring the set evaluation parameters of a plurality of pre-collected sample battery packs and corresponding safety indexes, wherein the safety indexes of each sample battery pack are set into scores from low to high according to safety as a first safety index value, a second safety index value and a third safety index value;
and screening out the set number of sample battery packs closest to the battery pack to be evaluated by adopting a proximity algorithm based on the set evaluation parameters, and determining the scores of most of the sample battery packs as the safety indexes of the battery pack to be evaluated.
4. The electric vehicle battery residual estimation method according to claim 3, wherein the basic data includes: initial internal resistance R of battery pack to be evaluatednewInternal resistance at end of life REOLThe internal resistance R in the current state; rated number of charges C of each sample battery packallWhen is coming into contact withNumber of pre-charging times Cnow(ii) a Voltage V of the nth single cell constituting the battery pack to be evaluated during operationnAnd the mean value of the voltage
Figure FDA0002930603570000028
Current discharge power Dp of battery pack to be evaluatednowRated discharge power DpratedCurrent charging power CpnowRated charging power CpratedHeating power Hp at factory shipmentnewCurrent heating power Hpnow；
Based on the basic data, the calculation formula of each residual value characteristic index is as follows:
Figure FDA0002930603570000021
Figure FDA0002930603570000022
Co＝Q3δ，
Figure FDA0002930603570000023
wherein Q is a constant, and delta is the standard deviation of the voltage of the single batteries forming the battery pack to be evaluated;
Figure FDA0002930603570000024
Figure FDA0002930603570000025
Figure FDA0002930603570000026
5. the method of claim 2, wherein the weight coefficient ω is a weight coefficient1、ω2、ω3……ω7Obtained by means of machine learning, including:
acquiring basic data of a plurality of sample battery packs, wherein the basic data comprises basic parameters of the sample battery packs and data generated by a battery management system in the driving process;
calculating residual value characteristic indexes corresponding to the sample battery packs according to the basic data to serve as input, adding battery residual values corresponding to the sample battery packs to serve as label output, and forming a sample data set; wherein the residual characteristic index includes: a health indicator, a remaining service life indicator, a single cell consistency indicator, and a safety indicator, and one or more of the following: a discharge capacity index, a charge capacity index, a heat level index;
training an initial model by using the sample data set to obtain a battery residual value evaluation model; wherein the initial model is
Figure FDA0002930603570000027
Extracting the weight coefficient in the battery residual value evaluation model as corresponding omega1、ω2、ω3……ω7。
6. The method for estimating battery residual of electric vehicle according to claim 5, further comprising, after forming the sample data set:
randomly sampling the sample data set according to a set proportion to obtain a training set, a verification set and a test set;
training an initial model by adopting the training set to obtain a battery residual value evaluation model; wherein the initial model is
Figure FDA0002930603570000031
Adopt theThe verification set carries out effect verification on the battery residual value evaluation model and carries out parameter tuning, and the weight coefficient in the tuned battery residual value evaluation model is extracted as the corresponding omega1、ω2、ω3……ω7；
And testing the optimized battery residual value evaluation model by adopting the test set.
7. The method for estimating the battery residual value of the electric vehicle as claimed in claim 6, wherein the step of testing the optimized battery residual value estimation model by using the test set comprises:
inputting the test set into the optimized battery residual value evaluation model, and calculating the root mean square error of the evaluation result, wherein the value of the root mean square error is inversely related to the accuracy of the battery residual value evaluation model.
8. The method according to claim 5, wherein the weight coefficients in the battery residual evaluation model are extracted as ω1、ω2、ω3……ω7The method also comprises the following steps:
training a plurality of battery residual value evaluation models, averaging the corresponding weight coefficients in each battery residual value evaluation model to obtain a fused weight coefficient, and generating the fused battery residual value evaluation model;
taking the weight coefficient in the fused battery residual value evaluation model as corresponding omega1、ω2、ω3……ω7。
9. The method for estimating battery residual of electric vehicle according to claim 1, before obtaining the basic data of the battery pack to be evaluated, further comprising:
inquiring basic parameters of the battery packs of various types of electric vehicles from a battery manufacturer or crawling the basic parameters from the Internet;
acquiring running data generated in the running process of a plurality of battery packs to be evaluated from a vehicle-enterprise monitoring platform or a national monitoring platform;
and storing the basic parameters of the battery packs of the electric automobiles of various models and the running data of the battery packs to be evaluated into a relational database or a big data storage system for direct acquisition during the estimation of the residual value of the battery.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 9.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112485695A (en) * 2020-11-23 2021-03-12 国网北京市电力公司 Detection method and device for power battery
CN112950030B (en) * 2021-03-02 2024-02-06 爱驰汽车有限公司 Residual error assessment method and device for electric automobile, electronic equipment and storage medium
CN113030761B (en) * 2021-04-08 2023-11-21 中国电力科学研究院有限公司 Method and system for evaluating battery health state of ultra-large-scale energy storage power station
CN113422407B (en) * 2021-06-09 2022-12-13 北京海博思创科技股份有限公司 Method and device for determining battery usable time and storage medium
CN113405743B (en) * 2021-06-16 2022-08-16 迈威科技(广州)有限公司 New energy electric vehicle production and manufacturing test data analysis processing method and system based on cloud computing and storage medium
CN113884910B (en) * 2021-10-19 2023-04-28 岚图汽车科技有限公司 Power battery residual value evaluation method, device, equipment and readable storage medium
CN116385032B (en) * 2023-01-17 2024-03-12 广州威尔森信息科技有限公司 New energy secondary handcart battery health assessment method and device
CN116298892A (en) * 2023-03-22 2023-06-23 江陵县铭焱盛世机电设备有限公司 Comprehensive battery life assessment method based on multidimensional analysis

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104346659A (en) * 2014-11-26 2015-02-11 北京邮电大学 Short-term power generation prediction method applied to high-concentration-ratio photovoltaic power generation system
CN109655754A (en) * 2018-12-14 2019-04-19 北京匠芯电池科技有限公司 A kind of cell performance evaluation method based on charging process multidimensional sublevel
CN109839602A (en) * 2019-02-02 2019-06-04 爱驰汽车（上海）有限公司 Power battery performance and Valuation Method, device, electronic equipment
CN110133508A (en) * 2019-04-24 2019-08-16 上海博强微电子有限公司 The safe early warning method of electric automobile power battery

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8260493B2 (en) * 2010-02-17 2012-09-04 GM Global Technology Operations LLC Health prognosis for complex system using fault modeling
CN103576098B (en) * 2012-07-19 2016-12-21 华为技术有限公司 A kind of power source life on-line monitoring method, system and power supply
KR101500128B1 (en) * 2013-08-23 2015-03-06 현대자동차주식회사 Degradation estimation method for high voltage battery
CN108037463B (en) * 2017-12-15 2020-01-21 太原理工大学 Lithium ion battery life prediction method
CN107918103B (en) * 2018-01-05 2023-06-09 广西大学 Lithium ion battery residual life prediction method based on gray particle filtering
CN108828448B (en) * 2018-06-08 2020-08-28 江苏大学 Battery state of charge on-line estimation method based on charging voltage curve fusion Kalman filtering
CN109934408A (en) * 2019-03-18 2019-06-25 常伟 A kind of application analysis method carrying out automobile batteries RUL prediction based on big data machine learning
CN110380144B (en) * 2019-06-12 2020-06-23 长沙理工大学 Mixed coordination control method and system for retired lithium iron phosphate and ternary lithium battery

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104346659A (en) * 2014-11-26 2015-02-11 北京邮电大学 Short-term power generation prediction method applied to high-concentration-ratio photovoltaic power generation system
CN109655754A (en) * 2018-12-14 2019-04-19 北京匠芯电池科技有限公司 A kind of cell performance evaluation method based on charging process multidimensional sublevel
CN109839602A (en) * 2019-02-02 2019-06-04 爱驰汽车（上海）有限公司 Power battery performance and Valuation Method, device, electronic equipment
CN110133508A (en) * 2019-04-24 2019-08-16 上海博强微电子有限公司 The safe early warning method of electric automobile power battery

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