WO2022242058A1 - 针对现实新能源汽车的电池健康状态估计方法 - Google Patents

针对现实新能源汽车的电池健康状态估计方法 Download PDF

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WO2022242058A1
WO2022242058A1 PCT/CN2021/129523 CN2021129523W WO2022242058A1 WO 2022242058 A1 WO2022242058 A1 WO 2022242058A1 CN 2021129523 W CN2021129523 W CN 2021129523W WO 2022242058 A1 WO2022242058 A1 WO 2022242058A1
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icp
battery
curve
soh
charging
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French (fr)
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王震坡
佘承其
刘鹏
张照生
林倪
武烨
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北京理工大学
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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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • 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
    • G01R31/005—Testing of electric installations on transport means
    • G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
    • 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]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • 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
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
    • 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
    • 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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Definitions

  • the invention belongs to the technical field of big data of new energy vehicles, and in particular relates to a method for detecting the health state of a power battery by using big data of new energy vehicles.
  • the power battery As the core component of the new energy vehicle power system, the power battery has an important impact on the driving performance, safety and other aspects of the vehicle. Therefore, power battery SOH estimation is also one of the hot issues in the field of new energy vehicles. .
  • power batteries often use the form of battery packs. Due to the limitations of actual conditions, it is difficult to obtain the real SOH of battery packs in actual operation through measurement methods. The daily charging and discharging behavior cannot meet the needs of test calibration, and even Executing the calibration also has the problems of huge workload and low efficiency.
  • the existing technology mainly focuses on performing operations such as cycle aging on the monomers that make up the battery pack in the test environment, such as using the incremental capacity (Incremental capacity, IC) method to estimate the SOH of the battery, and predicting the results through the SOH of the monomers. Indirectly reflect the SOH of the battery pack.
  • IC incremental capacity
  • the test results cannot be referred to each other.
  • the problems of heavy workload and low efficiency cannot be overcome.
  • the present invention provides a method for estimating the battery state of health of a real new energy vehicle, which specifically includes the following steps:
  • Step 1 From the real vehicle data of new energy vehicles collected by the on-board big data platform, select charging segments with constant current, the same current, and charging time greater than a predetermined value, and record the charging voltage, charging current, charging time and other data in the segment, And calculate the charging capacity between every two frames of data;
  • Step 2 calculating the capacity increment IC curve and the curve peak value ICP P for each charging segment screened in step 1;
  • Step 3 Establish an equivalent circuit model for the battery cells in the vehicle battery pack, and calculate the peak value ICP C of the equivalent IC curve of the battery pack according to the group form of the cells in the battery pack;
  • Step 4 Based on the empirical data of known materials and/or structural battery cells, establish a model relationship between the peak value of the IC curve of the battery cell and the SOH of the battery cell and fit the model parameters;
  • Step 5 Substitute the peak value ICP C of the equivalent IC curve obtained in step 3 into the model relationship in step 4, and finally obtain the complete battery pack SOH corresponding to the entire charging segment.
  • the charging capacity calculated between every two frames of data is specifically realized based on the ampere-hour integration method:
  • Q is the charging capacity
  • I is the current
  • t is the charging time
  • Q represents the charging capacity
  • U represents the charging voltage
  • d is the differential symbol
  • t is the charging time
  • the IC curve is obtained by using this formula, with the charging voltage as the abscissa and the IC value as the ordinate Coordinates, draw the IC curve, and extract the peak ICP of the IC curve in the high voltage interval.
  • a first-order equivalent circuit model is specifically established for the battery cell, and its formula is as follows:
  • U represents the terminal voltage of the single cell
  • E represents the electromotive force of the single cell
  • I represents the charging current of the single cell
  • R represents the internal resistance of the single cell.
  • the peak value ICP C of the equivalent IC curve of the battery pack is:
  • a first-order polynomial model relationship is specifically established between the peak value of the IC curve of the battery cell and the SOH of the battery cell:
  • a and B are the model parameters to be fitted respectively, and f represents the function.
  • step 4 a quadratic polynomial model relationship is specifically established between the peak value of the IC curve of the battery cell and the SOH of the battery cell:
  • C, D, E are the model parameters to be fitted respectively, and f represents the function.
  • step 4 is specifically obtained by performing a cycle aging test on battery cells of the same material and/or structure.
  • the above-mentioned method provided by the present invention effectively solves the problem of existing power battery by organically combining the existing and easily obtained single-level SOH prediction equation with the packet-level capacity incremental IC calculated from massive real-vehicle big data.
  • the state of the battery pack cannot be measured, and the estimation result cannot meet the technical problems of accuracy requirements, which avoids the consumption of manpower and material resources caused by a large number of cyclic aging tests, calibration and data processing of the monomer.
  • the present invention has many unexpected beneficial effects.
  • Fig. 1 is a schematic flow diagram of the overall process of the method provided by the present invention.
  • the method for estimating the battery state of health of a real new energy vehicle provided by the present invention, as shown in Figure 1, specifically includes the following steps:
  • Step 1 From the real vehicle data of new energy vehicles collected by the on-board big data platform, select charging segments with constant current, the same current, and charging time greater than a predetermined value, and record the charging voltage, charging current, charging time and other data in the segment, And calculate the charging capacity between every two frames of data;
  • Step 2 calculating the capacity increment IC curve and the curve peak value ICP P for each charging segment screened in step 1;
  • Step 3 Establish an equivalent circuit model for the battery cells in the vehicle battery pack, and calculate the peak value ICP C of the equivalent IC curve of the battery pack according to the group form of the cells in the battery pack;
  • Step 4 Based on the empirical data of known materials and/or structural battery cells, establish a model relationship between the peak value of the IC curve of the battery cell and the SOH of the battery cell and fit the model parameters;
  • Step 5 Substituting the peak value ICP C of the equivalent IC curve calculated using real vehicle big data in step 3 into the model relationship obtained by fitting the empirical data of the same type of monomer in step 4, and finally obtaining the complete Battery pack SOH.
  • the calculation of the charging capacity between every two frames of data in the step 1 is specifically based on the ampere-hour integration method:
  • Q is the charging capacity
  • I is the current
  • t is the charging time
  • the calculation of the capacity increment IC curve in the step 2 is obtained based on the following formula:
  • Q represents the charging capacity
  • U represents the charging voltage
  • d is the differential symbol
  • t is the charging time
  • the IC curve is obtained by using this formula, with the charging voltage as the abscissa and the IC value as the ordinate Coordinates, draw the IC curve, and extract the peak ICP of the IC curve in the high voltage interval.
  • a first-order equivalent circuit model is specifically established for the battery cell, and its formula is as follows:
  • U represents the terminal voltage of the single cell
  • E represents the electromotive force of the single cell
  • I represents the charging current of the single cell
  • R represents the internal resistance of the single cell.
  • the peak value ICP C of the equivalent IC curve of the battery pack is:
  • the battery pack is N-series and M-parallel (the cells are connected in parallel to form a module, and then the modules are connected in series to form a package) or M-parallel to N-series (the cells are first connected in series to form a module, and then the modules are connected in parallel to form a package), then Distributed calculations can be used to calculate the equivalent ICP after the monomers are grouped, and then consider the modules as monomers to calculate the equivalent ICP after the modules are grouped.
  • a first-order polynomial model relationship is specifically established between the peak value of the IC curve of the battery cell and the SOH of the battery cell:
  • a and B are the model parameters to be fitted respectively, and f represents the function.
  • step 4 a quadratic polynomial model relationship is specifically established between the peak value of the IC curve of the battery cell and the SOH of the battery cell:
  • C, D, E are the model parameters to be fitted respectively, and f represents the function.
  • the cycle aging test can be carried out on battery cells of the same material and/or structure, To supplement and improve the data required for the fitting process.
  • sequence number of each step in the embodiment of the present invention does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention .

Abstract

一种针对现实新能源汽车的电池健康状态估计方法，包括计算电池包容量增量值，绘制电池包容量增量曲线并提取曲线峰值ICP P，计算等效容量增量峰值ICP c并代入电池单体IC曲线峰值与电池单体SOH之间的模型关系，从而获得精确的电池包SOH。电池健康状态估计方法通过将现有的、容易得到的单体层面SOH预测方程，和海量实车大数据计算得到的包级别容量增量IC值有机结合，有效地解决了现有动力电池SOH估计中对电池包健康状态无法测量、估计结果无法满足精度要求的问题，避免了对电池包进行大量循环老化试验、标定与数据处理所带来的人力、物力消耗。

Description

针对现实新能源汽车的电池健康状态估计方法 技术领域
本发明属于新能源汽车大数据技术领域，具体涉及利用新能源汽车的大数据对动力电池健康状态进行检测的方法。
背景技术
动力电池作为新能源汽车动力系统中的核心组成部分，其健康状态对于车辆的行驶性能、安全性等多个方面都具有重要影响，因此动力电池SOH估计也是当前新能源汽车领域的热点问题之一。目前，动力电池常使用电池包的组成形式，由于现实条件的限制，电池包在实际运行中的真实SOH难以通过测量方式较为直接地得到，日常的充放电行为不能满足试验标定的需要，而即使执行标定也存在工作量巨大、效率较低的问题。现有技术主要集中于在试验环境对组成电池包的单体进行诸如循环老化等的操作，比如利用容量增量(Incremental capacity，IC)方法进行电池SOH的预估，通过单体的SOH预测结果间接反映电池包的SOH。然而，由于试验环境对车辆真实工况的模拟不甚完善，不同材料、结构电池之间的性能指标差异使得试验结果不能相互参考，为尽可能全面的覆盖各类工况以及扩充数据量，在利用上述的IC方法以及其他适合方法时，仍然无法克服工作量大与效率低的问题。
发明内容
新能源汽车相对于传统燃油车辆在车载数据收集与处理方面具有显著的优势，如果能较好地加以利用能够有效解决依赖海量数据的现有技术效率问题。有鉴于此，本发明提供了一种针对现实新能源汽车的电池健康状态估计方法，具体包括以下步骤：
步骤1、从车载大数据平台所收集的新能源汽车实车数据中筛选出电流恒定、电流相同、充电时间大于预定值的充电片段，记录片段中的充电电压、充电电流、充电时间等数据，并计算每两帧数据之间的充电容量；
步骤2、对步骤1中筛选的每个充电片段计算容量增量IC曲线以及曲线峰值ICP P；
步骤3、对车辆电池包中的电池单体建立等效电路模型，并根据电池包中单体的成组形式计算得到电池包的等效IC曲线峰值ICP C；
步骤4、基于已知材料和/或结构电池单体的经验数据，建立电池单体IC曲线峰值与电池单体SOH之间的模型关系并拟合模型参数；
步骤5、将步骤3中得到的等效IC曲线峰值ICP C代入步骤4中的模型关系，最终得到与整个充电片段对应的完整电池包SOH。
进一步地，所述步骤1中计算每两帧数据之间的充电容量具体基于安时积分 法实现：
Q＝I×t
其中，Q为充电容量，I为电流，t为充电时间。
进一步地，所述步骤2中计算容量增量IC曲线具体基于以下公式得到：
IC＝dQ/dU＝(Q t-Q t-1)/(U t-U t-1)
其中，Q表示充电容量，U表示充电电压，d为微分符号，t为充电时间；对筛选出的每一个充电片段，均利用该公式得到IC曲线，以充电电压为横坐标，IC值为纵坐标，绘制IC曲线，同时提取高电压区间IC曲线峰值ICP。
进一步地，所述步骤3中针对电池单体具体建立一阶等效电路模型，其公式如下：
U＝E+IR
其中，U表示单体电池端电压，E表示单体电池电动势，I表示单体充电电流，R表示单体电池内阻。
进一步地，针对单体的不同成组形式，电池包的等效IC曲线峰值ICP C分别为：
1)由N个单体串联组成的电池包，其ICP c＝N×ICP p；
2)由M个单体并联组成的电池包，其ICP c＝ICP p/M；
3)由N个单体先串联成模组、再由M个模组并联/由M个单体先并联成模组、再由N个模组串联组成的电池包，其ICP c＝N×ICP p/M。
进一步地，所述步骤4中具体在电池单体IC曲线峰值与电池单体SOH之间建立一次多项式模型关系：
SOH＝f(ICP c)＝A×ICP c+B
其中，A、B分别为待拟合的模型参数，f表示函数。
进一步地，所述步骤4中具体在电池单体IC曲线峰值与电池单体SOH之间建立二次多项式模型关系：
SOH＝f(ICP c)＝C×ICP c 2+D×ICP c+E
其中，C、D、E分别为待拟合的模型参数，f表示函数。
进一步地，所述步骤4中的经验数据具体通过对相同材料和/或结构的电池单体进行循环老化试验得到。
上述本发明所提供的方法，通过将现有的、容易得到的单体层面SOH预测方程，和海量实车大数据计算得到的包级别容量增量IC有机结合，有效地解决了现有动力电池SOH估计中对电池包状态无法测量，估计结果无法满足精度要求的技术问题，避免了对单体进行大量循环老化试验、标定与数据处理所带来的人力、物力消耗。相对于现有技术，本发明具有诸多预料不到的有益效果。
附图说明
图1为本发明所提供方法的总体流程示意图。
具体实施方式
下面将结合附图对本发明的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明所提供的针对现实新能源汽车的电池健康状态估计方法，如图1所示，具体包括以下步骤：
步骤1、从车载大数据平台所收集的新能源汽车实车数据中筛选出电流恒定、电流相同、充电时间大于预定值的充电片段，记录片段中的充电电压、充电电流、充电时间等数据，并计算每两帧数据之间的充电容量；
步骤2、对步骤1中筛选的每个充电片段计算容量增量IC曲线以及曲线峰值ICP P；
步骤3、对车辆电池包中的电池单体建立等效电路模型，并根据电池包中单体的成组形式计算得到电池包的等效IC曲线峰值ICP C；
步骤4、基于已知材料和/或结构电池单体的经验数据，建立电池单体IC曲线峰值与电池单体SOH之间的模型关系并拟合模型参数；
步骤5、将步骤3中利用实车大数据计算得到的等效IC曲线峰值ICP C，代入步骤4中利用同类型单体经验数据拟合得到的模型关系，最终得到与整个充电片段对应的完整电池包SOH。
在本发明的一个优选实施方式中，所述步骤1中计算每两帧数据之间的充电容量具体基于安时积分法实现：
Q＝I×t
其中，Q为充电容量，I为电流，t为充电时间。
在本发明的一个优选实施方式中，所述步骤2中计算容量增量IC曲线具体基于以下公式得到：
IC＝dQ/dU＝(Q t-Q t-1)/(U t-U t-1)
其中，Q表示充电容量，U表示充电电压，d为微分符号，t为充电时间；对筛选出的每一个充电片段，均利用该公式得到IC曲线，以充电电压为横坐标，IC值为纵坐标，绘制IC曲线，同时提取高电压区间IC曲线峰值ICP。众多研究表明，锂离子电池高电压区间ICP的下降与电池容量衰退具有紧密的相关性。
所述步骤3中针对电池单体具体建立一阶等效电路模型，其公式如下：
U＝E+IR
其中，U表示单体电池端电压，E表示单体电池电动势，I表示单体充电电流，R表示单体电池内阻。
针对单体的不同成组形式，电池包的等效IC曲线峰值ICP C分别为：
1)对于N个单体串联组成电池包，根据基尔霍夫定律，串联电路电流I相同，采集的端电压数据U T＝NU，则得N串单体的电池包等效ICP c：ICP c＝N×ICP p；
2)同理，对于M个单体并联组成的电池包，根据基尔霍夫定律，并联电路端电压U相同，因为并联电路电池容量为单体电池的M倍，如采用相同的充电倍率，则此时电池包的电流也应为单体M倍，即I p＝MI，则M个单体并联电池包等效ICP c：ICP c＝ICP p/M；
3)如果电池包是N串M并(单体先并联成模组，模组再串联成包)或M并N串(单体先串联成模组，模组再并联成包)结构，则可以分布计算，先计算单体成组后的等效ICP，再将模组看做单体，计算模组成组后的等效ICP。电池包等效ICP的最终表达式均为：ICP c＝N×ICP p/M。
在本发明的一个优选实施方式中，所述步骤4中具体在电池单体IC曲线峰值与电池单体SOH之间建立一次多项式模型关系：
SOH＝f(ICP c)＝A×ICP c+B
其中，A、B分别为待拟合的模型参数，f表示函数。
在本发明的一个优选实施方式中，所述步骤4中具体在电池单体IC曲线峰值与电池单体SOH之间建立二次多项式模型关系：
SOH＝f(ICP c)＝C×ICP c 2+D×ICP c+E
其中，C、D、E分别为待拟合的模型参数，f表示函数。
在本发明的一个优选实施方式中，对于现成的经验数据不甚完善、不足以拟合出较好的模型关系的情况，可通过对相同材料和/或结构的电池单体进行循环老化试验，来对拟合过程所需的数据进行补充和完善。
应理解，本发明实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本发明实施例的实施过程构成任何限定。
尽管已经示出和描述了本发明的实施例，对于本领域的普通技术人员而言，可以理解在不脱离本发明的原理和精神的情况下可以对这些实施例进行多种变化、修改、替换和变型，本发明的范围由所附权利要求及其等同物限定。

Claims (8)

  1. 针对现实新能源汽车的电池健康状态估计方法，其特征在于：具体包括以下步骤：
    步骤1、从车载大数据平台所收集的新能源汽车实车数据中筛选出电流恒定、电流相同、充电时间大于预定值的充电片段，记录片段中的充电电压、充电电流、充电时间等数据，并计算每两帧数据之间的充电容量；
    步骤2、对步骤1中筛选的每个充电片段计算容量增量IC曲线以及电池包曲线峰值ICP P；
    步骤3、对车辆电池包中的电池单体建立等效电路模型，并根据电池包中单体的成组形式计算得到电池包的等效IC曲线峰值ICP C；
    步骤4、基于已知材料和/或结构电池单体的经验数据，建立电池单体IC曲线峰值与电池单体SOH之间的模型关系并拟合模型参数；
    步骤5、将步骤3中得到的等效IC曲线峰值ICP C代入步骤4中的模型关系，最终得到与整个充电片段对应的完整电池包SOH。
  2. 如权利要求1所述的方法，其特征在于：所述步骤1中计算每两帧数据之间的充电容量具体基于安时积分法实现：
    Q＝I×t
    其中，Q为充电容量，I为电流，t为充电时间。
  3. 如权利要求1所述的方法，其特征在于：所述步骤2中计算容量增量IC曲线具体基于以下公式得到：
    IC＝dQ/dU＝(Q t-Q t-1)/(U t-U t-1)
    其中，Q表示充电容量，U表示充电电压，d为微分符号，t为充电时间；对筛选出的每一个充电片段，均利用该公式得到IC曲线，以充电电压为横坐标，IC值为纵坐标，绘制IC曲线，同时提取高电压区间IC曲线峰值ICP。
  4. 如权利要求1所述的方法，其特征在于：所述步骤3中针对电池单体具体建立一阶等效电路模型，其公式如下：
    U＝E+IR
    其中，U表示单体电池端电压，E表示单体电池电动势，I表示单体充电电流，R表示单体电池内阻。
  5. 如权利要求1所述的方法，其特征在于：步骤3中针对单体的不同成组形式，电池包的等效IC曲线峰值ICP C分别为：
    1)由N个单体串联组成的电池包，其ICP c＝N×ICP p；
    2)由M个单体并联组成的电池包，其ICP c＝ICP p/M；
    3)由N个单体先串联成模组、再由M个模组并联/由M个单体先并联成模组、再由N个模组串联组成的电池包，其ICP c＝N×ICP p/M。
  6. 如权利要求1所述的方法，其特征在于：所述步骤4中具体在电池单体IC曲线峰值与电池单体SOH之间建立一次多项式模型关系：
    SOH＝f(ICP c)＝A×ICP c+B
    其中，A、B分别为待拟合的模型参数，f表示函数。
  7. 如权利要求1所述的方法，其特征在于：所述步骤4中具体在电池单体IC曲线峰值与电池单体SOH之间建立二次多项式模型关系：
    SOH＝f(ICP c)＝C×ICP c 2+D×ICP c+E
    其中，C、D、E分别为待拟合的模型参数，f表示函数。
  8. 如权利要求1所述的方法，其特征在于：所述步骤4中的经验数据具体通过对相同材料和/或结构的电池单体进行循环老化试验来补充。
PCT/CN2021/129523 2021-05-21 2021-11-09 针对现实新能源汽车的电池健康状态估计方法 WO2022242058A1 (zh)

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