WO2022268144A1 - 一种基于两点阻抗老化特征的锂电池在线老化诊断方法 - Google Patents
一种基于两点阻抗老化特征的锂电池在线老化诊断方法 Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 147
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 147
- 230000032683 aging Effects 0.000 title claims abstract description 125
- 238000003745 diagnosis Methods 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000000157 electrochemical-induced impedance spectroscopy Methods 0.000 claims abstract description 24
- 238000012549 training Methods 0.000 claims abstract description 11
- 238000001453 impedance spectrum Methods 0.000 claims description 63
- 239000011159 matrix material Substances 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000009826 distribution Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 238000012417 linear regression Methods 0.000 claims description 3
- 238000013500 data storage Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
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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
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- 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
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- the invention belongs to a lithium battery online aging diagnosis method in the field of lithium battery research and development and application, and specifically relates to an online lithium battery aging diagnosis method based on two-point impedance aging characteristics.
- lithium batteries Due to many advantages such as high energy density, low cost, fast response to power demand, and long cycle life, lithium batteries have been commercially used in various fields on a large scale. Aging diagnosis technology plays an important role in the safe and reliable operation of lithium batteries. However, due to the complex aging mechanism of lithium batteries, and the aging path is affected by many factors in the design, production and application process, it is a challenge to achieve simple, fast and accurate lithium battery aging diagnosis under complex dynamic operating conditions .
- Electrochemical impedance spectroscopy is a method that can effectively detect the internal conditions of lithium batteries, and is widely used in the field of battery testing and research and development.
- the measurement time of electrochemical impedance spectroscopy is long, and it is difficult to implement it online to reflect the real-time aging state of lithium batteries, which has certain limitations. Therefore, taking relevant measures to apply electrochemical impedance spectroscopy to online monitoring of lithium battery aging status is of great significance for improving the reliability, safety and durability of lithium batteries.
- the present invention proposes an online aging diagnosis method for lithium batteries based on two-point impedance aging characteristics.
- the present invention comprises the following steps:
- the lithium battery aging diagnosis regression model is trained to obtain the trained lithium battery aging diagnosis regression model
- Two-point impedance aging characteristics input the best two-point impedance aging characteristics of the lithium battery to be diagnosed into the trained lithium battery aging diagnosis regression model for diagnosis, and output the total lithium battery capacity of the current lithium battery to be diagnosed, according to the lithium battery The total capacity judges the current aging state of the lithium battery to be diagnosed.
- Described step 2) specifically is:
- two different electrochemical impedance spectrum frequencies in the electrochemical impedance spectrum in each charge-discharge cycle of the current lithium battery are used as an electrochemical impedance spectrum frequency combination, and the comparison in an electrochemical impedance spectrum frequency combination is calculated.
- the difference between the impedance imaginary part of the high EIS frequency and the impedance imaginary part of the lower EIS frequency is used as a two-point impedance aging feature, and all EIS frequency combinations are traversed to obtain All two-point impedance aging characteristics of the current lithium battery in the current charge-discharge cycle.
- Described step 4) specifically is:
- the correlation coefficient between the two-point impedance aging characteristics of the same electrochemical impedance spectrum frequency combination in all charge and discharge cycles of all lithium batteries and the total capacity of the corresponding lithium batteries calculate the correlation coefficient between the two-point impedance aging characteristics of the same electrochemical impedance spectrum frequency combination in all charge and discharge cycles of all lithium batteries and the total capacity of the corresponding lithium batteries, and iterative calculation
- the correlation coefficients corresponding to all electrochemical impedance spectrum frequency combinations are obtained, and the correlation coefficient matrix is formed by the correlation coefficients corresponding to all electrochemical impedance spectrum frequency combinations, and the electrochemical impedance spectrum frequency combination corresponding to the correlation coefficient with the largest absolute value in the correlation coefficient matrix is taken as The best electrochemical impedance spectrum frequency combination, and then the two-point impedance aging characteristics corresponding to the best electrochemical impedance spectrum frequency combination as the best two-point impedance aging characteristics, and finally the best two-point impedance aging characteristics of all lithium batteries in all charge and discharge cycles
- the correlation coefficient is the Pearson correlation coefficient, specifically calculated by the following formula:
- ⁇ X, Y represent the correlation coefficient between the same electrochemical impedance spectrum frequency combination corresponding to the two-point impedance aging characteristics and the total capacity of the lithium battery in the corresponding charge and discharge cycle in all charge and discharge cycles of all lithium batteries
- X represents all lithium batteries
- Y represents the set of the total capacity of lithium batteries in all charge-discharge cycles of all lithium batteries
- E() represents the expected operation .
- step 6 The same charging state of charge in step 6) is the same as the specific charging state of charge in step 1).
- the lithium battery aging diagnosis regression model selects a linear regression model and a nonlinear regression model according to the distribution relationship between the best two-point impedance aging characteristics and the life of the lithium battery.
- the invention solves the problem of difficulty in online aging diagnosis of lithium batteries in practical applications.
- the two-point impedance aging feature based on electrochemical impedance spectroscopy is applied to the online aging diagnosis of lithium batteries.
- the imaginary part of the impedance can calculate the two-point impedance aging characteristics, and then accurately diagnose the aging state of the lithium battery, which reduces the burden of data storage, calculation and cost, and is more suitable for online aging diagnosis of lithium batteries in practical application scenarios. Lithium batteries run more safely and reliably.
- Fig. 1 is the overall flowchart of the present invention.
- Fig. 2 is a schematic diagram of the electrochemical impedance spectrum measured under a specific state of charge in each charge and discharge cycle of the lithium battery in the embodiment of the present invention and the impedance point corresponding to the selected optimal frequency combination of the electrochemical impedance spectrum.
- Fig. 3 is a graph showing the distribution relationship between the best two-point impedance aging characteristics and the corresponding total capacity of lithium batteries on the coordinate axis in all charge and discharge cycles of all lithium batteries in the embodiment of the present invention.
- Fig. 4 is a diagram of the training and test results of the lithium battery aging diagnosis regression model in the embodiment of the present invention.
- the present invention comprises the following steps:
- the specific charging state of charge is specifically a value in the 0%-100% state of charge.
- a specific implementation is a 100% state of charge.
- the total number of charge and discharge cycles is the total number of cycles when the total capacity of the lithium battery decays to 75% of the initial total capacity of the lithium battery.
- Step 2) is specifically:
- two different electrochemical impedance spectrum frequencies in the electrochemical impedance spectrum in each charge-discharge cycle of the current lithium battery are used as an electrochemical impedance spectrum frequency combination, and the comparison in an electrochemical impedance spectrum frequency combination is calculated.
- the difference between the impedance imaginary part of the high EIS frequency and the impedance imaginary part of the lower EIS frequency is used as a two-point impedance aging feature, and all EIS frequency combinations are traversed to obtain All two-point impedance aging characteristics of the current lithium battery in the current charge-discharge cycle.
- the preset electrochemical impedance spectrum frequency range is preferably 0.01999 Hz-20004.45300 Hz, and there are 60 measured electrochemical impedance spectrum frequencies, as shown in Table 1.
- Table 1 The higher the accuracy of the frequency of electrochemical impedance spectroscopy measured within the range allowed by the measuring equipment, the better.
- Step 4) is specifically:
- the electrochemical impedance spectrum frequency combination corresponding to the correlation coefficient with the largest absolute value in the correlation coefficient matrix is used as the best electrochemical impedance spectrum frequency combination, and then the two-point impedance aging characteristics corresponding to the best electrochemical impedance spectrum frequency combination are used as the best two.
- Point impedance aging characteristics as shown in Figure 2.
- the best two-point impedance aging characteristics of all lithium batteries in all charge and discharge cycles and the total capacity of lithium batteries in the corresponding charge and discharge cycles constitute a training set.
- the best two-point impedance aging characteristics are specifically the two-point impedance aging characteristics of all lithium batteries under the optimal electrochemical impedance spectrum frequency combination.
- the label of the two-point impedance aging feature under the impedance spectrum frequency combination is the total capacity of the lithium battery in the current charge and discharge cycle.
- the row number and column number of the correlation coefficient in the correlation coefficient matrix represent the two electrochemical impedance spectrum frequencies in the electrochemical impedance spectrum frequency combination corresponding to the two-point impedance aging characteristics, and the row and column numbers of the correlation coefficient matrix Both represent the preset EIS frequency range.
- the correlation coefficient is the Pearson correlation coefficient, specifically calculated by the following formula:
- ⁇ X, Y represent the correlation coefficient between the same electrochemical impedance spectrum frequency combination corresponding to the two-point impedance aging characteristics and the total capacity of the lithium battery in the corresponding charge and discharge cycle in all charge and discharge cycles of all lithium batteries
- X represents all lithium batteries
- Y represents the set of the total capacity of lithium batteries in all charge-discharge cycles of all lithium batteries
- E() represents the expected operation .
- the lithium battery aging diagnosis regression model is trained to obtain the trained lithium battery aging diagnosis regression model; the adaptive neuro-fuzzy system model is selected in the embodiment.
- the lithium battery aging diagnosis regression model selects the linear regression model and the nonlinear regression model according to the distribution relationship between the best two-point impedance aging characteristics and the life of the lithium battery.
- step 6 The same charging state of charge in step 6) is the same as the specific charging state of charge in step 1).
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Abstract
一种基于两点阻抗老化特征的锂电池在线老化诊断方法，包括以下步骤：1测量全新锂电池每次充放电循环中特定充电荷电状态下的电化学阻抗谱和锂电池总容量；2计算当前锂电池在各次充放电循环中不同频率组合对应的两点阻抗老化特征；3重复步骤1-2，获得各个锂电池在各次充放电循环中所有两点阻抗老化特征和总容量；4选取所有锂电池在各次充放电循环中的最佳两点阻抗老化特征和对应锂电池总容量构成训练集；5获得训练后的锂电池老化诊断回归模型；6在线诊断时，测量并计算待诊断锂电池的最佳两点阻抗老化特征，诊断后获得待诊断锂电池总容量，从而判断老化状态。该方法实现精确的锂电池老化诊断，有助更安全可靠的运行。
Description
本发明属于锂电池研发与应用的领域的一种锂电池在线老化诊断方法，具体涉及了一种基于两点阻抗老化特征的锂电池在线老化诊断方法。
由于具有能量密度高、成本低、功率需求响应快、循环寿命长等众多优势，锂电池被大规模的商业化应用于各个领域。老化诊断技术对于锂电池的安全可靠运行具有重要作用。然而，由于锂电池具有复杂的老化机理，且老化路径受设计、生产和应用过程中的诸多因素影响，使得在复杂动态运行条件下实现简单、快速和精确的锂电池老化诊断成为了一项挑战。此外，对于由数千个单体锂电池组成的大型电池组，由于制造和运行条件的差异，每个单体电池之间会不可避免的存在着内在和外在差异，因此不能将整个电池组视为一个电池，而需要对其中的每个单体电池分别进行老化诊断，这将带来巨大的数据存储负担、计算负担和成本负担。解决以上问题的有效方案包括老化诊断算法的改进和老化诊断特征的改进。然而，当前大量相关研究都集中在开发更好的算法上，而很少关注开发更好的特征。目前大部分实际应用中都采用总容量参数来表示锂电池的老化状态，当锂电池的老化诊断特征足够好时，使用简单的回归模型便能够实现准确的锂电池总容量诊断。因此，设计和开发更好的锂电池老化诊断特征具有着重要意义。
电化学阻抗谱是一种可以有效检测锂电池内部状况的手段，广泛运用于电池检测与研发领域。但电化学阻抗谱测量时间较长，难以在线实施以反映锂电池实时老化状态，具有一定的局限性。因此，采取相关措施将电化学阻抗谱技术运用于锂电池老化状态在线监测，对于提升锂电池的可靠性、安全性和耐久性具有重大意义。
发明内容
为了解决现有技术的不足，本发明提出了一种基于两点阻抗老化特征的锂电池在线老化诊断方法。
本发明采用的方案是：
本发明包括以下步骤：
1)测量全新锂电池每次充放电循环中特定充电荷电状态下的电化学阻抗谱和锂电池总容量；
2)计算当前锂电池在各次充放电循环中所有电化学阻抗谱频率组合对应的两点阻抗老化特征；
3)重复步骤1)-2)对各个锂电池均进行处理，获得各个锂电池在各次充放电循环中所有电化学阻抗谱频率组合对应的两点阻抗老化特征和锂电池总容量；
4)根据各个锂电池的所有电化学阻抗谱频率组合对应的两点阻抗老化特征，选取最佳电化学阻抗谱频率组合，将最佳电化学阻抗谱频率组合对应的两点阻抗老化特征作为最佳两点阻抗老化特征，选取所有锂电池在各次充放电循环中的最佳两点阻抗老化特征和对应锂电池总容量构成训练集；
5)基于训练集对锂电池老化诊断回归模型进行训练，获得训练后的锂电池老化诊断回归模型；
6)在线诊断时，仅采集待诊断锂电池在当前次充放电循环中相同充电荷电状态下测量得到的最佳电化学阻抗谱频率组合对应的阻抗值，计算出待诊断锂电池的最佳两点阻抗老化特征，将待诊断锂电池的最佳两点阻抗老化特征输入到训练好的锂电池老化诊断回归模型中进行诊断，输出获得当前待诊断锂电池的锂电池总容量，根据锂电池总容量判断当前待诊断锂电池的老化状态。
所述步骤2)具体为：
在预设频率范围中，当前锂电池在每次充放电循环中的电化学阻抗谱中两个不同电化学阻抗谱频率作为一个电化学阻抗谱频率组合，计算一个电化学阻抗谱频率组合中较高的电化学阻抗谱频率的阻抗虚部与较低的电化学阻抗谱频率的阻抗虚部的差值并将该差值作为一个两点阻抗老化特征，遍历所有电化学阻抗谱频率组合，获得当前锂电池在当前次充放电循环中的所有两点阻抗老化特征。
所述步骤4)具体为：
根据所有锂电池的所有两点阻抗老化特征，计算所有锂电池的所有充放电循环中相同电化学阻抗谱频率组合的两点阻抗老化特征与对应锂电池的总容量之间的相关系数，遍历计算获得所有电化学阻抗谱频率组合对应的相关系数，由所有电化学阻抗谱频率组合对应的相关系数构成相关系数矩阵，将相关系数矩阵中绝对值最大的相关系数对应的电化学阻抗谱频率组合作为最佳电化学阻抗谱频率组合，然后将最佳电化学阻抗谱频率组合对应的两点阻抗老化特征作为最佳两点阻抗老化特征，最后将所有锂电池在所有充放电循环中的最佳两点 阻抗老化特征和对应充放电循环中的锂电池总容量构成训练集。
所述相关系数为皮尔森相关系数，具体通过以下公式进行计算：
其中，ρ
X,Y表示所有锂电池的所有充放电循环中相同电化学阻抗谱频率组合对应两点阻抗老化特征与对应充放电循环中锂电池总容量之间的相关系数，X表示所有锂电池的所有充放电循环中相同电化学阻抗谱频率组合对应的两点阻抗老化特征的集合，Y表示所有锂电池在各自所有充放电循环中的锂电池总容量的集合，E()表示取期望操作。
所述步骤6)中相同充电荷电状态与步骤1)中的特定充电荷电状态相同。
所述锂电池老化诊断回归模型根据最佳两点阻抗老化特征与锂电池的寿命的分布关系选择线性回归模型和非线性回归模型。
本发明的有益效果是：
本发明解决了实际应用中锂电池在线老化诊断困难的问题。将基于电化学阻抗谱的两点阻抗老化特征应用到锂电池的在线老化诊断上，仅通过测量锂电池在每次充放电循环中一个特定充电荷电状态下两个电化学阻抗谱频率对应的阻抗虚部值便可计算出两点阻抗老化特征，进而精确诊断锂电池老化状态，降低了数据存储负担、计算负担和成本负担，更适合实际应用场景中锂电池的在线老化诊断，有助于锂电池更加安全可靠的运行。
图1是本发明的整体流程图。
图2是本发明实施例中锂电池各次充放电循环中特定充电荷电状态下测量得到的电化学阻抗谱以及选取的最佳电化学阻抗谱频率组合对应的阻抗点示意图。
图3是本发明实施例中所有锂电池的所有充放电循环中最佳两点阻抗老化特征与对应锂电池总容量在坐标轴上的分布关系图。
图4是本发明实施例中锂电池老化诊断回归模型的训练和测试结果图。
下面结合附图和具体实施例对本发明进行详细说明。
如图1所示，本发明包括以下步骤：
1)测量全新锂电池每次充放电循环中特定充电荷电状态下的电化学阻抗谱和锂电池总容量；特定充电荷电状态具体为0％-100％荷电状态中的某一个值。 具体实施时为100％的荷电状态。充放电循环的总次数为锂电池总容量衰减到初始锂电池总容量的75％时的总循环次数。
2)计算当前锂电池在各次充放电循环中所有电化学阻抗谱频率组合对应的两点阻抗老化特征；
步骤2)具体为：
在预设频率范围中，当前锂电池在每次充放电循环中的电化学阻抗谱中两个不同电化学阻抗谱频率作为一个电化学阻抗谱频率组合，计算一个电化学阻抗谱频率组合中较高的电化学阻抗谱频率的阻抗虚部与较低的电化学阻抗谱频率的阻抗虚部的差值并将该差值作为一个两点阻抗老化特征，遍历所有电化学阻抗谱频率组合，获得当前锂电池在当前次充放电循环中的所有两点阻抗老化特征。具体实施中，预设电化学阻抗谱频率范围优选为0.01999Hz-20004.45300Hz，测量的电化学阻抗谱频率共有60个，如表1所示。在测量设备允许的范围内测量的电化学阻抗谱频率的精度越高越好，测量电化学阻抗谱频率的精度越高，电化学阻抗谱频率组合就越多，对应的两点阻抗老化特征也越多。
表1电化学阻抗谱测量的所有电化学阻抗谱频率(Hz)
3)重复步骤1)-2)对各个锂电池均进行处理，获得各个锂电池在各次充放电循环中所有电化学阻抗谱频率组合对应的两点阻抗老化特征和总容量；
4)根据各个锂电池的所有电化学阻抗谱频率组合对应的两点阻抗老化特征，选取最佳电化学阻抗谱频率组合，将最佳电化学阻抗谱频率组合对应的两点阻抗老化特征作为最佳两点阻抗老化特征，选取所有锂电池在各次充放电循环中的最佳两点阻抗老化特征和对应锂电池总容量构成训练集；
步骤4)具体为：
根据所有锂电池的所有两点阻抗老化特征，计算所有锂电池的所有充放电循环中相同电化学阻抗谱频率组合的两点阻抗老化特征与对应锂电池的总容量之间的相关系数，遍历计算获得所有电化学阻抗谱频率组合对应的相关系数，由所有电化学阻抗谱频率组合对应的相关系数构成相关系数矩阵，相关系数矩阵作为不同电化学阻抗谱频率组合对应的两点阻抗老化特征与锂电池总容量之 间相关性的紧凑表示，如表2所示。
表2相关系数矩阵局部示意表
将相关系数矩阵中绝对值最大的相关系数对应的电化学阻抗谱频率组合作为最佳电化学阻抗谱频率组合，然后将最佳电化学阻抗谱频率组合对应的两点阻抗老化特征作为最佳两点阻抗老化特征，如图2所示。最后将所有锂电池在所有充放电循环中的最佳两点阻抗老化特征和对应充放电循环中的锂电池总容量构成训练集。如图3所示，其中，最佳两点阻抗老化特征具体为所有锂电池在最佳电化学阻抗谱频率组合下的两点阻抗老化特征，每个充放电循环中锂电池在最佳电化学阻抗谱频率组合下的两点阻抗老化特征的标签为当前充放电循环中锂电池的总容量。如表2所示，相关系数矩阵中相关系数的行号和列号分别表示两点阻抗老化特征对应的电化学阻抗谱频率组合中的两个电化学阻抗谱频率，相关系数矩阵的行和列均表示预设电化学阻抗谱频率范围。
所述相关系数为皮尔森相关系数，具体通过以下公式进行计算：
其中，ρ
X,Y表示所有锂电池的所有充放电循环中相同电化学阻抗谱频率组合对应两点阻抗老化特征与对应充放电循环中锂电池总容量之间的相关系数，X表示所有锂电池的所有充放电循环中相同电化学阻抗谱频率组合对应的两点阻抗老化特征的集合，Y表示所有锂电池在各自所有充放电循环中的锂电池总容量的集合，E()表示取期望操作。
5)基于训练集对锂电池老化诊断回归模型进行训练，获得训练后的锂电池老化诊断回归模型；实施例中选用的是自适应神经模糊***模型。锂电池老化诊断回归模型根据最佳两点阻抗老化特征与锂电池的寿命的分布关系选择线性 回归模型和非线性回归模型。
6)在线诊断时，仅采集待诊断锂电池在当前次充放电循环中相同充电荷电状态下测量得到的最佳电化学阻抗谱频率组合对应的阻抗值，计算出待诊断锂电池的最佳两点阻抗老化特征，将待诊断锂电池的最佳两点阻抗老化特征输入到训练好的锂电池老化诊断回归模型中进行诊断，输出获得当前待诊断锂电池的锂电池总容量，根据锂电池总容量判断当前待诊断锂电池的老化状态，如图4所示。
步骤6)中相同充电荷电状态与步骤1)中的特定充电荷电状态相同。
Claims (6)
- 一种基于两点阻抗老化特征的锂电池在线老化诊断方法，其特征在于，包括以下步骤：1)测量全新锂电池每次充放电循环中特定充电荷电状态下的电化学阻抗谱和锂电池总容量；2)计算当前锂电池在各次充放电循环中所有电化学阻抗谱频率组合对应的两点阻抗老化特征；3)重复步骤1)-2)对各个锂电池均进行处理，获得各个锂电池在各次充放电循环中所有电化学阻抗谱频率组合对应的两点阻抗老化特征和锂电池总容量；4)根据各个锂电池的所有电化学阻抗谱频率组合对应的两点阻抗老化特征，选取最佳电化学阻抗谱频率组合，将最佳电化学阻抗谱频率组合对应的两点阻抗老化特征作为最佳两点阻抗老化特征，选取所有锂电池在各次充放电循环中的最佳两点阻抗老化特征和对应锂电池总容量构成训练集；5)基于训练集对锂电池老化诊断回归模型进行训练，获得训练后的锂电池老化诊断回归模型；6)在线诊断时，仅采集待诊断锂电池在当前次充放电循环中相同充电荷电状态下测量得到的最佳电化学阻抗谱频率组合对应的阻抗值，计算出待诊断锂电池的最佳两点阻抗老化特征，将待诊断锂电池的最佳两点阻抗老化特征输入到训练好的锂电池老化诊断回归模型中进行诊断，输出获得当前待诊断锂电池的锂电池总容量，根据锂电池总容量判断当前待诊断锂电池的老化状态。
- 根据权利要求1所述的一种基于两点阻抗老化特征的锂电池在线老化诊断方法，其特征在于，所述步骤2)具体为：在预设频率范围中，当前锂电池在每次充放电循环中的电化学阻抗谱中两个不同电化学阻抗谱频率作为一个电化学阻抗谱频率组合，计算一个电化学阻抗谱频率组合中较高的电化学阻抗谱频率的阻抗虚部与较低的电化学阻抗谱频率的阻抗虚部的差值并将该差值作为一个两点阻抗老化特征，遍历所有电化学阻抗谱频率组合，获得当前锂电池在当前次充放电循环中的所有两点阻抗老化特征。
- 根据权利要求1所述的一种基于电化学阻抗谱两点阻抗老化特征的锂电池在线老化方法，其特征在于，所述步骤4)具体为：根据所有锂电池的所有两点阻抗老化特征，计算所有锂电池的所有充放电 循环中相同电化学阻抗谱频率组合的两点阻抗老化特征与对应锂电池的总容量之间的相关系数，遍历计算获得所有电化学阻抗谱频率组合对应的相关系数，由所有电化学阻抗谱频率组合对应的相关系数构成相关系数矩阵，将相关系数矩阵中绝对值最大的相关系数对应的电化学阻抗谱频率组合作为最佳电化学阻抗谱频率组合，然后将最佳电化学阻抗谱频率组合对应的两点阻抗老化特征作为最佳两点阻抗老化特征，最后将所有锂电池在所有充放电循环中的最佳两点阻抗老化特征和对应充放电循环中的锂电池总容量构成训练集。所述步骤6)中相同充电荷电状态与步骤1)中的特定充电荷电状态相同。
- 根据权利要求3所述的一种基于两点阻抗老化特征的锂电池在线老化诊断方法，其特征在于，所述相关系数为皮尔森相关系数，具体通过以下公式进行计算：
其中，ρ X,Y表示所有锂电池的所有充放电循环中相同电化学阻抗谱频率组合对应两点阻抗老化特征与对应充放电循环中锂电池总容量之间的相关系数，X表示所有锂电池的所有充放电循环中相同电化学阻抗谱频率组合对应的两点阻抗老化特征的集合，Y表示所有锂电池在各自所有充放电循环中的锂电池总容量的集合，E()表示取期望操作。 - 根据权利要求1所述的一种基于两点阻抗老化特征的锂电池在线老化诊断方法，其特征在于，所述步骤6)中相同充电荷电状态与步骤1)中的特定充电荷电状态相同。
- 根据权利要求1所述的一种基于两点阻抗老化特征的锂电池在线老化诊断方法，其特征在于，所述锂电池老化诊断回归模型根据最佳两点阻抗老化特征与锂电池的寿命的分布关系选择线性回归模型和非线性回归模型。
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