WO2022237660A1 - Lithium battery online aging diagnosis method based on two-point aging characteristics - Google Patents
Lithium battery online aging diagnosis method based on two-point aging characteristics Download PDFInfo
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- 230000032683 aging Effects 0.000 title claims abstract description 124
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- the invention belongs to the field of lithium battery application and relates to an online aging diagnosis method for lithium batteries, and relates to an online aging diagnosis method for lithium batteries based on two-point 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 .
- the present invention proposes an online aging diagnosis method for lithium batteries based on two-point 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
- the charging voltage and charging capacity corresponding to the best charging voltage combination in the kth charging and discharging cycle of the lithium battery to be diagnosed and the current charging and discharging cycle after the kth time are respectively collected, and the best charging voltage combination is calculated.
- the best two-point aging characteristics to be predicted input the best two-point aging characteristics to be predicted 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 total capacity of the lithium battery, the current aging state of the lithium battery to be diagnosed is judged.
- Each voltage-capacity curve in the step 2) is subtracted from the voltage-capacity reference curve, and the capacity difference curve corresponding to each charge-discharge cycle is calculated and obtained, specifically:
- the step 3) is specifically:
- two different charging voltages in each capacity difference curve are regarded as a charging voltage combination, and the absolute value of the difference between the capacity difference vectors corresponding to a charging voltage combination is calculated and used as a two-point aging characteristic , traverse all charging voltage combinations, obtain all two-point aging characteristics of the current capacity difference curve, traverse each capacity difference curve, and calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charge-discharge cycle.
- the step 5) is specifically:
- the correlation coefficient matrix is formed by the correlation coefficients corresponding to all charging voltage combinations, and the charging voltage combination corresponding to the correlation coefficient with the largest absolute value in the correlation coefficient matrix is taken as the optimal charging voltage Combination, and then the two-point aging characteristics corresponding to the best charging voltage combination as the best two-point aging characteristics, and finally the best two-point aging characteristics of all lithium batteries in all charge and discharge cycles and the corresponding lithium batteries in charge and discharge cycles
- the total battery capacity constitutes the training set.
- the correlation coefficient is the Pearson correlation coefficient, specifically calculated by the following formula:
- ⁇ X, Y represents the correlation coefficient between the two-point aging characteristics corresponding to the same charging voltage combination in all charging and discharging cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charging and discharging cycles, and X represents all charging and discharging cycles of all lithium batteries.
- the set of two-point aging characteristics corresponding to the same charging voltage combination in the discharge cycle, Y represents the set of the total capacity of lithium batteries of all lithium batteries in all charge and discharge cycles, and E() represents the expected operation.
- the charging mode of each charging and discharging cycle of the lithium battery is the same as the charging mode of the voltage-capacity reference curve in the preset charging voltage range, and the charging mode varies according to different battery models.
- 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 characteristics and the total capacity of the lithium battery.
- the invention solves the problem of difficulty in online aging diagnosis of lithium batteries in practical applications.
- the two-point aging characteristics can be calculated by monitoring the capacity values corresponding to two fixed charging voltage points in each charge-discharge cycle of the lithium battery during online application, and then Realize accurate lithium battery aging diagnosis, reduce data storage burden, calculation burden and cost burden, and do not need to rely on specific discharge test data that does not exist in actual lithium battery applications, and are more suitable for online aging diagnosis of lithium batteries in actual application scenarios. Contribute to the safer and more reliable operation of lithium batteries.
- Fig. 1 is the overall flowchart of the present invention.
- Fig. 2 is a schematic diagram of calculating the capacity difference vectors of the first charge-discharge cycle and the sixth charge-discharge cycle in the same charging mode and the same stage of the lithium battery in the embodiment of the present invention and within the same voltage range.
- Fig. 3 is a position diagram of two charging voltages corresponding to the optimal two-point aging characteristics selected in the embodiment of the present invention on different capacity difference curves of a lithium battery.
- Fig. 4 is a graph showing the distribution relationship between the best two-point aging characteristics of all lithium batteries selected in the embodiment of the present invention in all charge and discharge cycles and the corresponding total capacity of lithium batteries on the logarithmic axis of 10.
- the present invention comprises the following steps:
- step 2) each voltage capacity curve is subtracted from the voltage capacity reference curve to calculate and obtain the capacity difference curve corresponding to each voltage capacity curve, specifically:
- Step 3) is specifically:
- the preset charging voltage range two different charging voltages in each capacity difference curve are regarded as a charging voltage combination, and the absolute value of the difference between the capacity difference vectors corresponding to a charging voltage combination is calculated and used as a two-point aging characteristic , traverse all charging voltage combinations, obtain all two-point aging characteristics of the current capacity difference curve, traverse each capacity difference curve, and calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charge-discharge cycle.
- the preset charging voltage range is preferably 3.05V-4.20V. The higher the accuracy of the charging voltage within the range allowed by the sensor accuracy, the better. The higher the accuracy of the charging voltage, the more charging voltage combinations, and the more corresponding two-point aging characteristics.
- Step 5) is specifically:
- the correlation coefficient of all charging voltage combinations is obtained through traversal calculation, and the correlation coefficient matrix is formed by the correlation coefficients of all charging voltage combinations.
- the correlation coefficient matrix is used as the correlation between the two-point aging characteristics corresponding to different charging voltage combinations and the total capacity of the lithium battery.
- the charging voltage combination corresponding to the correlation coefficient with the largest absolute value in the correlation coefficient matrix is taken as the best charging voltage combination, and then the two aging characteristics corresponding to the best charging voltage combination are taken as the best two points Aging characteristics.
- the best two-point 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 aging characteristics are specifically the two-point aging characteristics of all lithium batteries under the best charging voltage combination in different charge and discharge cycles, and the best charging voltage combination in each charge and discharge cycle
- the label of the two-point aging characteristic is the total lithium battery capacity of the current lithium battery in the current charge and discharge cycle.
- the row number and column number of the correlation coefficient in the correlation coefficient matrix respectively represent the two charging voltages in the charging voltage combination corresponding to the two-point aging characteristics, and the row and column of the correlation coefficient matrix both represent the preset charging voltage range .
- the correlation coefficient is the Pearson correlation coefficient, which is calculated by the following formula:
- ⁇ X, Y represents the correlation coefficient between the two-point aging characteristics corresponding to the same charging voltage combination in all charging and discharging cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charging and discharging cycles, and X represents all charging and discharging cycles of all lithium batteries.
- the set of two-point aging characteristics corresponding to the same charging voltage combination in the discharge cycle, Y represents the set of the total capacity of lithium batteries of all lithium batteries in all charge and discharge cycles, and E() represents the expected operation.
- 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 aging characteristics and the total capacity of the lithium battery.
- the charging voltage and charging capacity corresponding to the best charging voltage combination in the kth charging and discharging cycle of the lithium battery to be diagnosed and the current charging and discharging cycle after the kth time are respectively collected, and the best charging voltage combination is calculated.
- the best two-point aging characteristics to be predicted input the best two-point aging characteristics to be predicted 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 total capacity of the lithium battery, the current aging state of the lithium battery to be diagnosed is judged.
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Abstract
Disclosed is a lithium battery online aging diagnosis method based on two-point aging characteristics. The present invention comprises the following steps: 1 collecting and calculating a voltage capacity reference curve of a current lithium battery; 2 calculating and obtaining a capacity difference curve corresponding to each charge-discharge cycle of the current lithium battery; 3 calculating two-point aging characteristics corresponding to all charging voltage combinations of each charge-discharge cycle of the current lithium battery; 4 repeating 1-3 to obtain two-point aging characteristics of each charge-discharge cycle of each lithium battery and the total capacities of the lithium batteries; 5 selecting an optimal charging voltage combination and an optimal two-point aging characteristic, and forming a training set; 6 obtaining a trained lithium battery aging diagnosis regression model; and 7 collecting and calculating, during online diagnosis, the optimal two-point aging characteristic to be predicted of a lithium battery to be diagnosed, and obtaining the total capacity of the lithium battery after diagnosis so as to determine the aging state of the lithium battery to be diagnosed. The present invention achieves accurate lithium battery aging diagnosis, and the data storage burden, calculation burden and cost burden are reduced.
Description
本发明属于锂电池应用的领域的一种锂电池在线老化诊断方法，涉及一种基于两点老化特征的锂电池在线老化诊断方法。The invention belongs to the field of lithium battery application and relates to an online aging diagnosis method for lithium batteries, and relates to an online aging diagnosis method for lithium batteries based on two-point aging characteristics.
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。同时，由于绝大多数实际应用场景中锂电池的放电模式都是随机的，不能基于特定的放电测试数据来提取特征进行老化诊断。相反地，锂电池的充电模式在大多数场景下都是固定的。以车用锂电池为例，一般仅存在两种充电模式，即普通充电模式和快充模式。因此，直接基于锂电池的充电传感数据设计开发更好的老化诊断特征具有着重要意义。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 . In addition, for a large battery pack composed of thousands of individual lithium batteries, due to differences in manufacturing and operating conditions, there will inevitably exist inherent and extrinsic differences between each individual battery, so the entire battery pack cannot Considering it as a battery, it is necessary to carry out aging diagnosis for each single battery in it, which will bring a huge data storage burden, calculation burden and cost burden. Effective solutions to the above problems include the improvement of aging diagnosis algorithms and the improvement of aging diagnosis features. However, a large amount of current related research focuses on developing better algorithms, while little attention is paid to developing better features. At present, in most practical applications, the total capacity parameter is used to represent the aging state of the lithium battery. When the aging diagnosis characteristics of the lithium battery are good enough, a simple regression model can be used to achieve accurate diagnosis of the total capacity of the lithium battery. At the same time, since the discharge mode of lithium batteries in most practical application scenarios is random, it is not possible to extract features based on specific discharge test data for aging diagnosis. On the contrary, the charging mode of lithium battery is fixed in most scenarios. Taking lithium batteries for vehicles as an example, generally there are only two charging modes, namely normal charging mode and fast charging mode. Therefore, it is of great significance to design and develop better aging diagnostic features directly based on the charge sensing data of lithium batteries.
发明内容Contents of the invention
为了解决现有技术的不足，本发明提出了一种基于两点老化特征的锂电池在线老化诊断方法。In order to solve the deficiencies of the prior art, the present invention proposes an online aging diagnosis method for lithium batteries based on two-point aging characteristics.
本发明采用的方案是：The scheme that the present invention adopts is:
本发明包括以下步骤：The present invention comprises the following steps:
1)采集全新锂电池前20次充放电循环中的充电电压和充电容量，选择第k次充放电循环中的充电电压和充电容量，k＝1,2,…,20，获得电压容量曲线并作为当前锂电池的电压容量基准曲线；1) Collect the charging voltage and charging capacity in the first 20 charge-discharge cycles of the new lithium battery, select the charging voltage and charge capacity in the k-th charge-discharge cycle, k=1,2,...,20, obtain the voltage-capacity curve and As the voltage capacity benchmark curve of the current lithium battery;
2)依次采集当前锂电池每次充放电循环中的充电电压和充电容量以及锂电 池总容量，获得每次充放电循环对应的电压容量曲线，每条电压容量曲线与电压容量基准曲线相减，计算获得各次充放电循环对应的容量差曲线；2) Sequentially collect the charging voltage and charging capacity and the total capacity of the lithium battery in each charge-discharge cycle of the current lithium battery, and obtain the voltage-capacity curve corresponding to each charge-discharge cycle, and subtract each voltage-capacity curve from the voltage-capacity reference curve, Calculate and obtain the capacity difference curve corresponding to each charge and discharge cycle;
3)计算当前锂电池各次充放电循环的所有充电电压组合对应的两点老化特征；3) Calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charging and discharging cycle of the current lithium battery;
4)重复步骤1)-3)对各个锂电池均进行处理，获得各个锂电池的各次充放电循环的两点老化特征和对应的充放电循环中锂电池总容量；4) Repeat steps 1)-3) to process each lithium battery to obtain the two-point aging characteristics of each charge-discharge cycle of each lithium battery and the total capacity of the lithium battery in the corresponding charge-discharge cycle;
5)根据所有锂电池的各次充放电循环的所有两点老化特征，选取最佳充电电压组合，将最佳充电电压组合对应的两点老化特征作为最佳两点老化特征，由所有锂电池的最佳两点老化特征和对应的充放电循环中锂电池总容量构成训练集；5) According to all the two-point aging characteristics of each charge-discharge cycle of all lithium batteries, select the best charging voltage combination, and use the two-point aging characteristics corresponding to the best charging voltage combination as the best two-point aging characteristics, and all lithium batteries The best two-point aging characteristics and the corresponding total capacity of the lithium battery in the charge-discharge cycle constitute the training set;
6)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) Based on the training set, the lithium battery aging diagnosis regression model is trained to obtain the trained lithium battery aging diagnosis regression model;
7)在线诊断时，分别采集待诊断锂电池的第k次充放电循环和第k次以后的当次充放电循环中最佳充电电压组合对应的充电电压和充电容量，计算最佳充电电压组合对应的待预测的最佳两点老化特征，将待预测的最佳两点老化特征输入到训练后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。7) During online diagnosis, the charging voltage and charging capacity corresponding to the best charging voltage combination in the kth charging and discharging cycle of the lithium battery to be diagnosed and the current charging and discharging cycle after the kth time are respectively collected, and the best charging voltage combination is calculated. Corresponding to the best two-point aging characteristics to be predicted, input the best two-point aging characteristics to be predicted 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 total capacity of the lithium battery, the current aging state of the lithium battery to be diagnosed is judged.
所述步骤2)中每条电压容量曲线与电压容量基准曲线相减，计算获得各次充放电循环对应的容量差曲线，具体为：Each voltage-capacity curve in the step 2) is subtracted from the voltage-capacity reference curve, and the capacity difference curve corresponding to each charge-discharge cycle is calculated and obtained, specifically:
将每条电压容量曲线与电压容量基准曲线中每个充电电压对应的两个容量值相减获得差向量作为该充电电压的容量差向量，绘制成当前充放电循环的容量差曲线，容量差曲线中的横坐标为充电电压，容量差曲线中的纵坐标为容量差向量，遍历各次充放电循环对应的各条电压容量曲线，计算获得各次充放电循环对应的容量差曲线。Subtract each voltage capacity curve from the two capacity values corresponding to each charging voltage in the voltage capacity reference curve to obtain a difference vector as the capacity difference vector of the charging voltage, and draw the capacity difference curve of the current charge and discharge cycle, the capacity difference curve The abscissa in is the charging voltage, and the ordinate in the capacity difference curve is the capacity difference vector, traverse each voltage-capacity curve corresponding to each charge-discharge cycle, and calculate and obtain the capacity difference curve corresponding to each charge-discharge cycle.
所述步骤3)具体为：The step 3) is specifically:
在预设充电电压范围中，每条容量差曲线中两个不同充电电压作为一个充电电压组合，计算一个充电电压组合对应的容量差向量之间的差值的绝对值并作为一个两点老化特征，遍历所有充电电压组合，获得当前容量差曲线的所有两点老化特征，遍历每条容量差曲线，计算各次充放电循环的所有充电电压组合对应的两点老化特征。In the preset charging voltage range, two different charging voltages in each capacity difference curve are regarded as a charging voltage combination, and the absolute value of the difference between the capacity difference vectors corresponding to a charging voltage combination is calculated and used as a two-point aging characteristic , traverse all charging voltage combinations, obtain all two-point aging characteristics of the current capacity difference curve, traverse each capacity difference curve, and calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charge-discharge cycle.
所述步骤5)具体为：The step 5) is specifically:
根据所有锂电池的各次充放电循环的所有两点老化特征，计算所有锂电池的所有充放电循环中相同充电电压组合对应的两点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，遍历计算获得所有充电电压组合对应的相关系数，由所有充电电压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充电电压组合作为最佳充电电压组合，然后将最佳充电电压组合对应的两点老化特征作为最佳两点老化特征，最后将所有锂电池在各自所有充放电循环中的最佳两点老化特征和对应的充放电循环中锂电池总容量构成训练集。According to all the two-point aging characteristics of each charge-discharge cycle of all lithium batteries, calculate the relationship between the two-point aging characteristics corresponding to the same charging voltage combination in all charge-discharge cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charge-discharge cycles Correlation coefficient, traversal calculation to obtain the correlation coefficients corresponding to all charging voltage combinations, the correlation coefficient matrix is formed by the correlation coefficients corresponding to all charging voltage combinations, and the charging voltage combination corresponding to the correlation coefficient with the largest absolute value in the correlation coefficient matrix is taken as the optimal charging voltage Combination, and then the two-point aging characteristics corresponding to the best charging voltage combination as the best two-point aging characteristics, and finally the best two-point aging characteristics of all lithium batteries in all charge and discharge cycles and the corresponding lithium batteries in charge and discharge cycles The total battery capacity constitutes the training set.
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：The correlation coefficient is the Pearson correlation coefficient, specifically calculated by the following formula:
其中，ρ
X,Y表示所有锂电池的所有充放电循环中相同充电电压组合对应的两点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充电电压组合对应的两点老化特征的集合，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。
Among them, ρ X, Y represents the correlation coefficient between the two-point aging characteristics corresponding to the same charging voltage combination in all charging and discharging cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charging and discharging cycles, and X represents all charging and discharging cycles of all lithium batteries. The set of two-point aging characteristics corresponding to the same charging voltage combination in the discharge cycle, Y represents the set of the total capacity of lithium batteries of all lithium batteries in all charge and discharge cycles, and E() represents the expected operation.
所述锂电池每次充放电循环的充电模式与电压容量基准曲线的充电模式在预设充电电压范围中相同，所述的充电模式根据电池型号不同而改变。The charging mode of each charging and discharging cycle of the lithium battery is the same as the charging mode of the voltage-capacity reference curve in the preset charging voltage range, and the charging mode varies according to different battery models.
所述锂电池老化诊断回归模型根据最佳两点特征与锂电池总容量的分布关系选择线性回归模型和非线性回归模型。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 characteristics and the total capacity of the lithium battery.
本发明的有益效果是：The beneficial effects of the present invention are:
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。将两点老化特征应用到锂电池的在线老化诊断上，在线应用时仅需监测锂电池在每次充放电循环中两个固定充电电压点对应的容量值便可以计算出两点老化特征，进而实现精确的锂电池老化诊断，降低了数据存储负担、计算负担和成本负担，且无需依赖实际锂电池应用中不存在的特定放电测试数据，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。The invention solves the problem of difficulty in online aging diagnosis of lithium batteries in practical applications. Applying the two-point aging characteristics to the online aging diagnosis of lithium batteries, the two-point aging characteristics can be calculated by monitoring the capacity values corresponding to two fixed charging voltage points in each charge-discharge cycle of the lithium battery during online application, and then Realize accurate lithium battery aging diagnosis, reduce data storage burden, calculation burden and cost burden, and do not need to rely on specific discharge test data that does not exist in actual lithium battery applications, and are more suitable for online aging diagnosis of lithium batteries in actual application scenarios. Contribute to the safer and more reliable operation of lithium batteries.
图1是本发明的整体流程图。Fig. 1 is the overall flowchart of the present invention.
图2是本发明实施例中锂电池相同充电模式相同阶段且相同电压范围内第一次充放电循环与第六次充放电循环的容量差向量计算示意图。Fig. 2 is a schematic diagram of calculating the capacity difference vectors of the first charge-discharge cycle and the sixth charge-discharge cycle in the same charging mode and the same stage of the lithium battery in the embodiment of the present invention and within the same voltage range.
图3是本发明实施例中选取的最佳两点老化特征对应的两个充电电压在一个锂电池的不同容量差曲线上的位置图。Fig. 3 is a position diagram of two charging voltages corresponding to the optimal two-point aging characteristics selected in the embodiment of the present invention on different capacity difference curves of a lithium battery.
图4是本发明实施例中选取的所有锂电池在各自所有充放电循环中的最佳两点老化特征与对应的锂电池总容量在10的对数坐标轴上的分布关系图。Fig. 4 is a graph showing the distribution relationship between the best two-point aging characteristics of all lithium batteries selected in the embodiment of the present invention in all charge and discharge cycles and the corresponding total capacity of lithium batteries on the logarithmic axis of 10.
下面结合附图和具体实施例对本发明进行详细说明。The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
如图1所示，本发明包括以下步骤：As shown in Figure 1, the present invention comprises the following steps:
1)采集全新锂电池前20次充放电循环中的充电电压和充电容量，选择第k次充放电循环中的充电电压和充电容量，获得电压容量曲线并作为当前锂电池的电压容量基准曲线；k＝1,2,…,20；具体实施中，k＝1，采集全新锂电池第一次充放电循环中的充电电压和充电容量，获得当前锂电池的电压容量基准曲线。1) Collect the charging voltage and charging capacity in the first 20 charge-discharge cycles of the new lithium battery, select the charging voltage and charge capacity in the kth charge-discharge cycle, obtain the voltage-capacity curve and use it as the voltage-capacity benchmark curve of the current lithium battery; k=1,2,...,20; in specific implementation, k=1, collect the charging voltage and charging capacity in the first charging and discharging cycle of the new lithium battery, and obtain the voltage capacity reference curve of the current lithium battery.
2)依次采集当前锂电池每次充放电循环中的充电电压和充电容量以及锂电池总容量，即采集第k次以后的充放电循环中的充电电压和充电容量以及锂电池总容量，获得每次充放电循环对应的电压容量曲线，如图2所示，每条电压容量曲线与电压容量基准曲线相减，计算获得各条电压容量曲线对应的容量差曲线，即每次充放电循环对应的容量差曲线，如图3所示；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80％时的总循环次数。锂电池每次充放电循环的充电模式与电压容量基准曲线的充电模式在预设充电电压范围中相同，锂电池的充电模式根据电池型号不同而改变。2) Sequentially collect the charging voltage, charging capacity and total capacity of the lithium battery in each charge-discharge cycle of the current lithium battery, that is, collect the charging voltage, charge capacity and total capacity of the lithium battery in the charge-discharge cycle after the kth time, and obtain each The voltage-capacity curve corresponding to each charge-discharge cycle is shown in Figure 2. Each voltage-capacity curve is subtracted from the voltage-capacity reference curve to calculate and obtain the capacity difference curve corresponding to each voltage-capacity curve, that is, the capacity difference curve corresponding to each charge-discharge cycle. The capacity difference curve is shown in Figure 3; the total number of charge and discharge cycles is the total number of cycles when the total capacity of the lithium battery decays to 80% of the total capacity of the initial lithium battery. The charging mode of each charge-discharge cycle of the lithium battery is the same as the charging mode of the voltage-capacity reference curve in the preset charging voltage range, and the charging mode of the lithium battery varies according to the battery model.
步骤2)中每条电压容量曲线与电压容量基准曲线相减，计算获得各条电压容量曲线对应的容量差曲线，具体为：In step 2), each voltage capacity curve is subtracted from the voltage capacity reference curve to calculate and obtain the capacity difference curve corresponding to each voltage capacity curve, specifically:
将每条电压容量曲线与电压容量基准曲线中每个充电电压对应的两个容量值相减获得差向量作为该充电电压的容量差向量，绘制成当前充放电循环的容量差曲线，容量差曲线中的横坐标为充电电压，容量差曲线中的纵坐标为容量差向量，遍历各次充放电循环对应的各条电压容量曲线，计算获得各次充放电循环对应的容量差曲线，如图3所示。Subtract each voltage capacity curve from the two capacity values corresponding to each charging voltage in the voltage capacity reference curve to obtain a difference vector as the capacity difference vector of the charging voltage, and draw the capacity difference curve of the current charge and discharge cycle, the capacity difference curve The abscissa in the graph is the charging voltage, and the ordinate in the capacity difference curve is the capacity difference vector, traverse each voltage-capacity curve corresponding to each charge-discharge cycle, and calculate and obtain the capacity difference curve corresponding to each charge-discharge cycle, as shown in Figure 3 shown.
3)计算当前锂电池各次充放电循环的所有充电电压组合对应的两点老化特征；3) Calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charging and discharging cycle of the current lithium battery;
步骤3)具体为：Step 3) is specifically:
在预设充电电压范围中，每条容量差曲线中两个不同充电电压作为一个充电电压组合，计算一个充电电压组合对应的容量差向量之间的差值的绝对值并作为一个两点老化特征，遍历所有充电电压组合，获得当前容量差曲线的所有两点老化特征，遍历每条容量差曲线，计算各次充放电循环的所有充电电压组合对应的两点老化特征。具体实施中，预设充电电压范围优选为3.05V-4.20V。在传感器精度允许的范围内充电电压的精度越高越好，充电电压的精度越高，充电电压组合就越多，对应的两点老化特征也越多。In the preset charging voltage range, two different charging voltages in each capacity difference curve are regarded as a charging voltage combination, and the absolute value of the difference between the capacity difference vectors corresponding to a charging voltage combination is calculated and used as a two-point aging characteristic , traverse all charging voltage combinations, obtain all two-point aging characteristics of the current capacity difference curve, traverse each capacity difference curve, and calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charge-discharge cycle. In specific implementation, the preset charging voltage range is preferably 3.05V-4.20V. The higher the accuracy of the charging voltage within the range allowed by the sensor accuracy, the better. The higher the accuracy of the charging voltage, the more charging voltage combinations, and the more corresponding two-point aging characteristics.
4)重复步骤1)-3)对各个锂电池均进行处理，获得各个锂电池的各次充放电 循环的两点老化特征和对应的充放电循环中锂电池总容量；所有锂电池的型号相同。4) Repeat steps 1)-3) to process each lithium battery to obtain the two-point aging characteristics of each charge-discharge cycle of each lithium battery and the total capacity of the lithium battery in the corresponding charge-discharge cycle; all lithium batteries have the same model .
5)根据所有锂电池的各次充放电循环的所有两点老化特征，选取最佳充电电压组合，将最佳充电电压组合对应的两点老化特征作为最佳两点老化特征，由所有锂电池的最佳两点老化特征和对应的充放电循环中锂电池总容量构成训练集；5) According to all the two-point aging characteristics of each charge-discharge cycle of all lithium batteries, select the best charging voltage combination, and use the two-point aging characteristics corresponding to the best charging voltage combination as the best two-point aging characteristics, and all lithium batteries The best two-point aging characteristics and the corresponding total capacity of the lithium battery in the charge-discharge cycle constitute the training set;
步骤5)具体为：Step 5) is specifically:
根据所有锂电池的各次充放电循环的所有两点老化特征，计算所有锂电池的所有充放电循环中相同充电电压组合的两点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，遍历计算获得所有充电电压组合的相关系数，由所有充电电压组合的相关系数构成相关系数矩阵，相关系数矩阵作为不同充电电压组合对应的两点老化特征与锂电池总容量之间相关性的紧凑表示，如表1所示，将相关系数矩阵中绝对值最大的相关系数对应的充电电压组合作为最佳充电电压组合，然后将最佳充电电压组合对应的两点老化特征作为最佳两点老化特征，最后将所有锂电池在各自所有充放电循环中的最佳两点老化特征和对应的充放电循环中锂电池总容量构成训练集。如图4所示，其中，最佳两点老化特征具体为所有锂电池在不同充放电循环中在最佳充电电压组合下的两点老化特征，每次充放电循环中最佳充电电压组合下的两点老化特征的标签为当前充放电循环中当前锂电池的锂电池总容量。如表1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老化特征对应的充电电压组合中的两个充电电压，相关系数矩阵的行和列均表示预设充电电压范围。According to all the two-point aging characteristics of each charging and discharging cycle of all lithium batteries, calculate the correlation between the two-point aging characteristics of the same charging voltage combination in all charging and discharging cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charging and discharging cycles The correlation coefficient of all charging voltage combinations is obtained through traversal calculation, and the correlation coefficient matrix is formed by the correlation coefficients of all charging voltage combinations. The correlation coefficient matrix is used as the correlation between the two-point aging characteristics corresponding to different charging voltage combinations and the total capacity of the lithium battery. Compact representation, as shown in Table 1, the charging voltage combination corresponding to the correlation coefficient with the largest absolute value in the correlation coefficient matrix is taken as the best charging voltage combination, and then the two aging characteristics corresponding to the best charging voltage combination are taken as the best two points Aging characteristics. Finally, the best two-point 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. As shown in Figure 4, the best two-point aging characteristics are specifically the two-point aging characteristics of all lithium batteries under the best charging voltage combination in different charge and discharge cycles, and the best charging voltage combination in each charge and discharge cycle The label of the two-point aging characteristic is the total lithium battery capacity of the current lithium battery in the current charge and discharge cycle. As shown in Table 1, the row number and column number of the correlation coefficient in the correlation coefficient matrix respectively represent the two charging voltages in the charging voltage combination corresponding to the two-point aging characteristics, and the row and column of the correlation coefficient matrix both represent the preset charging voltage range .
表1 相关系数矩阵局部示意表Table 1 Local representation of correlation coefficient matrix
相关系数为皮尔森相关系数，具体通过以下公式进行计算：The correlation coefficient is the Pearson correlation coefficient, which is calculated by the following formula:
其中，ρ
X,Y表示所有锂电池的所有充放电循环中相同充电电压组合对应的两点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充电电压组合对应的两点老化特征的集合，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。
Among them, ρ X, Y represents the correlation coefficient between the two-point aging characteristics corresponding to the same charging voltage combination in all charging and discharging cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charging and discharging cycles, and X represents all charging and discharging cycles of all lithium batteries. The set of two-point aging characteristics corresponding to the same charging voltage combination in the discharge cycle, Y represents the set of the total capacity of lithium batteries of all lithium batteries in all charge and discharge cycles, and E() represents the expected operation.
6)将训练集输入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系统模型。锂电池老化诊断回归模型根据最佳两点老化特征与锂电池总容量的分布关系选择线性回归模型和非线性回归模型。6) Input the training set into the lithium battery aging diagnosis regression model for training, and 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 aging characteristics and the total capacity of the lithium battery.
7)在线诊断时，分别采集待诊断锂电池的第k次充放电循环和第k次以后的当次充放电循环中最佳充电电压组合对应的充电电压和充电容量，计算最佳充电电压组合对应的待预测的最佳两点老化特征，将待预测的最佳两点老化特征输入到训练后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。7) During online diagnosis, the charging voltage and charging capacity corresponding to the best charging voltage combination in the kth charging and discharging cycle of the lithium battery to be diagnosed and the current charging and discharging cycle after the kth time are respectively collected, and the best charging voltage combination is calculated. Corresponding to the best two-point aging characteristics to be predicted, input the best two-point aging characteristics to be predicted 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 total capacity of the lithium battery, the current aging state of the lithium battery to be diagnosed is judged.
Claims (7)
- 一种基于两点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：A lithium battery online aging diagnosis method based on two-point aging characteristics, is characterized in that, comprises the following steps:1)采集全新锂电池前20次充放电循环中的充电电压和充电容量，选择第k次充放电循环中的充电电压和充电容量，k＝1,2,…,20，获得电压容量曲线并作为当前锂电池的电压容量基准曲线；1) Collect the charging voltage and charging capacity in the first 20 charge-discharge cycles of the new lithium battery, select the charging voltage and charge capacity in the k-th charge-discharge cycle, k=1,2,...,20, obtain the voltage-capacity curve and As the voltage capacity benchmark curve of the current lithium battery;2)依次采集当前锂电池每次充放电循环中的充电电压和充电容量以及锂电池总容量，获得每次充放电循环对应的电压容量曲线，每条电压容量曲线与电压容量基准曲线相减，计算获得各次充放电循环对应的容量差曲线；2) Sequentially collect the charging voltage and charging capacity and the total capacity of the lithium battery in each charge-discharge cycle of the current lithium battery, and obtain the voltage-capacity curve corresponding to each charge-discharge cycle, and subtract each voltage-capacity curve from the voltage-capacity reference curve, Calculate and obtain the capacity difference curve corresponding to each charge and discharge cycle;3)计算当前锂电池各次充放电循环的所有充电电压组合对应的两点老化特征；3) Calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charging and discharging cycle of the current lithium battery;4)重复步骤1)-3)对各个锂电池均进行处理，获得各个锂电池的各次充放电循环的两点老化特征和对应的充放电循环中锂电池总容量；4) Repeat steps 1)-3) to process each lithium battery to obtain the two-point aging characteristics of each charge-discharge cycle of each lithium battery and the total capacity of the lithium battery in the corresponding charge-discharge cycle;5)根据所有锂电池的各次充放电循环的所有两点老化特征，选取最佳充电电压组合，将最佳充电电压组合对应的两点老化特征作为最佳两点老化特征，由所有锂电池的最佳两点老化特征和对应的充放电循环中锂电池总容量构成训练集；5) According to all the two-point aging characteristics of each charge-discharge cycle of all lithium batteries, select the best charging voltage combination, and use the two-point aging characteristics corresponding to the best charging voltage combination as the best two-point aging characteristics, and all lithium batteries The best two-point aging characteristics and the corresponding total capacity of the lithium battery in the charge-discharge cycle constitute the training set;6)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) Based on the training set, the lithium battery aging diagnosis regression model is trained to obtain the trained lithium battery aging diagnosis regression model;7)在线诊断时，分别采集待诊断锂电池的第k次充放电循环和第k次以后的当次充放电循环中最佳充电电压组合对应的充电电压和充电容量，计算最佳充电电压组合对应的待预测的最佳两点老化特征，将待预测的最佳两点老化特征输入到训练后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。7) During online diagnosis, the charging voltage and charging capacity corresponding to the best charging voltage combination in the kth charging and discharging cycle of the lithium battery to be diagnosed and the current charging and discharging cycle after the kth time are respectively collected, and the best charging voltage combination is calculated. Corresponding to the best two-point aging characteristics to be predicted, input the best two-point aging characteristics to be predicted 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 total capacity of the lithium battery, the current aging state of the lithium battery to be diagnosed is judged.
- 根据权利要求1所述的一种基于两点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)中每条电压容量曲线与电压容量基准曲线相减，计算获得各次充放电循环对应的容量差曲线，具体为：A lithium battery online aging diagnosis method based on two-point aging characteristics according to claim 1, characterized in that, in the step 2), each voltage capacity curve is subtracted from the voltage capacity reference curve to calculate and obtain each charge The capacity difference curve corresponding to the discharge cycle is as follows:将每条电压容量曲线与电压容量基准曲线中每个充电电压对应的两个容量值相减获得差向量作为该充电电压的容量差向量，绘制成当前充放电循环的容量差曲线，容量差曲线中的横坐标为充电电压，容量差曲线中的纵坐标为容量差向量，遍历各次充放电循环对应的各条电压容量曲线，计算获得各次充放电循环对应的容量差曲线。Subtract each voltage capacity curve from the two capacity values corresponding to each charging voltage in the voltage capacity reference curve to obtain a difference vector as the capacity difference vector of the charging voltage, and draw the capacity difference curve of the current charge and discharge cycle, the capacity difference curve The abscissa in is the charging voltage, and the ordinate in the capacity difference curve is the capacity difference vector, traverse each voltage-capacity curve corresponding to each charge-discharge cycle, and calculate and obtain the capacity difference curve corresponding to each charge-discharge cycle.
- 根据权利要求1所述的一种基于两点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3)具体为：A kind of lithium battery online aging diagnosis method based on two-point aging characteristics according to claim 1, characterized in that, said step 3) is specifically:在预设充电电压范围中，每条容量差曲线中两个不同充电电压作为一个充电电压组合，计算一个充电电压组合对应的容量差向量之间的差值的绝对值并作为一个两点老化特征，遍历所有充电电压组合，获得当前容量差曲线的所有两点老化特征，遍历每条容量差曲线，计算各次充放电循环的所有充电电压组合对应的两点老化特征。In the preset charging voltage range, two different charging voltages in each capacity difference curve are regarded as a charging voltage combination, and the absolute value of the difference between the capacity difference vectors corresponding to a charging voltage combination is calculated and used as a two-point aging characteristic , traverse all charging voltage combinations, obtain all two-point aging characteristics of the current capacity difference curve, traverse each capacity difference curve, and calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charge-discharge cycle.
- 根据权利要求1所述的一种基于两点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5)具体为：A kind of lithium battery online aging diagnosis method based on two-point aging characteristics according to claim 1, characterized in that, said step 5) is specifically:根据所有锂电池的各次充放电循环的所有两点老化特征，计算所有锂电池的所有充放电循环中相同充电电压组合对应的两点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，遍历计算获得所有充电电压组合对应的相关系数，由所有充电电压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充电电压组合作为最佳充电电压组合，然后将最佳充电电压组合对应的两点老化特征作为最佳两点老化特征，最后将所有锂电池在各自所有充放电循环中的最佳两点老化特征和对应的充放电循环中锂电池总容量构成训练集。According to all the two-point aging characteristics of each charge-discharge cycle of all lithium batteries, calculate the relationship between the two-point aging characteristics corresponding to the same charging voltage combination in all charge-discharge cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charge-discharge cycles Correlation coefficient, traversal calculation to obtain the correlation coefficients corresponding to all charging voltage combinations, the correlation coefficient matrix is formed by the correlation coefficients corresponding to all charging voltage combinations, and the charging voltage combination corresponding to the correlation coefficient with the largest absolute value in the correlation coefficient matrix is taken as the optimal charging voltage Combination, and then the two-point aging characteristics corresponding to the best charging voltage combination as the best two-point aging characteristics, and finally the best two-point aging characteristics of all lithium batteries in all charge and discharge cycles and the corresponding lithium batteries in charge and discharge cycles The total battery capacity constitutes the training set.
- 根据权利要求4所述的一种基于两点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：An online aging diagnosis method for lithium batteries based on two-point aging characteristics according to claim 4, wherein the correlation coefficient is a Pearson correlation coefficient, specifically calculated by the following formula:
其中，ρ X,Y表示所有锂电池的所有充放电循环中相同充电电压组合对应的两点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充电电压组合对应的两点老化特征的集合，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。 Among them, ρ X, Y represents the correlation coefficient between the two-point aging characteristics corresponding to the same charging voltage combination in all charging and discharging cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charging and discharging cycles, and X represents all charging and discharging cycles of all lithium batteries. The set of two-point aging characteristics corresponding to the same charging voltage combination in the discharge cycle, Y represents the set of the total capacity of lithium batteries of all lithium batteries in all charge and discharge cycles, and E() represents the expected operation. - 根据权利要求1所述的一种基于两点老化特征的锂电池在线老化诊断方法，其特征在于，所述锂电池每次充放电循环的充电模式与电压容量基准曲线的充电模式在预设充电电压范围中相同，所述的充电模式根据电池型号不同而改变。An online aging diagnosis method for lithium batteries based on two-point aging characteristics according to claim 1, wherein the charging mode of each charging and discharging cycle of the lithium battery and the charging mode of the voltage capacity reference curve are within the preset charging The voltage range is the same, and the charging mode described varies according to the battery model.
- 根据权利要求1所述的一种基于两点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特征与锂电池总 容量的分布关系选择线性回归模型和非线性回归模型。A lithium battery online aging diagnosis method based on two-point aging characteristics according to claim 1, wherein the lithium battery aging diagnosis regression model selects linearity according to the distribution relationship between the best two-point characteristics and the total capacity of the lithium battery Regression models and nonlinear regression models.
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