CN111126449A - Battery fault classification diagnosis method based on cluster analysis - Google Patents

Battery fault classification diagnosis method based on cluster analysis Download PDF

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CN111126449A
CN111126449A CN201911206611.0A CN201911206611A CN111126449A CN 111126449 A CN111126449 A CN 111126449A CN 201911206611 A CN201911206611 A CN 201911206611A CN 111126449 A CN111126449 A CN 111126449A
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单毅
吴定国
张兵
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Gotion High Tech Co Ltd
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Abstract

The invention relates to a battery fault classification diagnosis method based on cluster analysis, which takes data such as pressure difference, temperature difference, average current, average vehicle speed and the like recorded by a battery in a period of time as 4 important indexes of cluster induction. After normalization processing, the data mining of cluster analysis can be carried out on various problems recorded by the battery by using a K-means method. The method can be used for mining the association between the battery data from a deeper level and better classifying various batteries. And then the efficiency of the manager is higher during information retrieval, and the obtained result is more complete.

Description

Battery fault classification diagnosis method based on cluster analysis
Technical Field
The invention relates to the technical field of batteries, in particular to a battery fault classification diagnosis method based on cluster analysis.
Background
Before the data mining technology is expanded to battery performance induction analysis mining, battery module data is used as the most effective carrier for battery quantitative analysis, and a large amount of technical information is hidden. The traditional patent data mining has the problems of low efficiency, single dimension, small data sample, insufficient deep level and the like, so that the current requirements on the battery performance data mining cannot be met.
Disclosure of Invention
The battery fault classification diagnosis method based on cluster analysis can deeply mine the association among data, better classify the battery performance data and enable the clustering result to be more integral.
In order to achieve the purpose, the invention adopts the following technical scheme:
a battery fault classification diagnosis method based on cluster analysis comprises the following steps:
s100, collecting and sorting battery data, and taking the power battery pressure difference, the temperature difference, the average current and the average speed as clustering variables;
and S200, clustering by using a K-means method.
Further, in the step S100, the differential pressure, the temperature difference, the average current and the average speed of the power battery are used as clustering variables; the method specifically comprises the following steps:
s101, recording the difference between the maximum value and the minimum value of the monomer voltage in the battery module as a pressure difference, and recording the difference between the maximum value and the minimum value of the monomer temperature as a temperature difference; taking the date as a period of the data recorded according to a certain sampling frequency, and calculating an average value as a clustering variable;
and S102, calculating the average value and the average speed value of the current of the battery in each day of use as a clustering variable.
Further, the S200 performs clustering by using a K-means method, which specifically includes:
s201, selecting K initial central points as clustering centers;
s202, in the Nth iteration, calculating the distance from any sample to K centers, and classifying the sample to the class where the center closest to the sample is located;
s203, recalculating the average value of all the points in each cluster, and updating the average value to a new cluster center;
and S204, repeating the processes of the second step and the third step until the cluster center does not change or is smaller than a given threshold value.
Further, in S201, K initial central points are selected as clustering centers; the method specifically comprises the following steps:
and determining the value of K by adopting an SSE method, wherein a specific algorithm is as follows:
Figure BDA0002297067560000021
wherein, ciIs the ith cluster, p is ciSample point of (1), miIs ciThe centroid of (1), SSE, is the clustering error of all samples, and represents how good the clustering effect is.
Further, in the nth iteration, the distance from any sample to the K centers is calculated, and the sample is classified into the class where the center closest to the sample is located; the method specifically comprises the following steps:
let DklRepresents GkAnd GlThe sum of squared deviations formula is as follows:
Dki＝Wm-Wk-Wi
in the formula:
Figure BDA0002297067560000022
Figure BDA0002297067560000023
are respectively class GkClass GlAnd a center of gravity of the Gm-like.
Further, the step S202 further includes:
converting the original data into a standard Z score by adopting a standard normal conversion mode, wherein the calculation formula is as follows:
Figure BDA0002297067560000031
the method adopts the squared Euclidean distance, the original data contains p variables, and each sample is a point in a p-dimensional space;
when two samples are represented by x ═ (x1, x2, …, xp) and y ═ y1, y2, …, yp, the squared euclidean distance between the p variables of the two samples is calculated as follows:
Figure BDA0002297067560000032
further, the step S100 of collecting and arranging battery data includes data of differences of reaction monomers in the battery module, data of use conditions of the electric vehicle, and data of performance of the entire use of the battery.
According to the technical scheme, the battery fault classification diagnosis method based on the cluster analysis is a battery overall performance cluster analysis method based on the K-means method; according to the method, 4 important evaluation indexes of the power battery pressure difference, the temperature difference, the average current and the average speed are simultaneously selected as clustering variables for clustering analysis. The method can deeply mine the correlation among the battery performance data, better classify the patent data, enable the clustering result to be more integral, and make up the defects of the traditional battery fault analysis method judged by experience.
The method can deeply mine the association among the battery data, better classify various batteries, and enable managers to have higher efficiency and more complete results in information retrieval.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a flow diagram of the present invention;
FIG. 3 is a graph of the cluster analysis of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
As shown in fig. 1, the method for classifying and diagnosing battery faults based on cluster analysis in this embodiment includes:
the method comprises the following specific steps:
step1, taking the differential pressure, the temperature difference, the average current and the average vehicle speed of the power battery as clustering variables;
step2, K-means clustering;
the clustering variable calculating method in the Step1 comprises the following steps:
the difference between the maximum value and the minimum value of the monomer voltage recorded in the Step1.1 battery module is pressure difference; the difference between the maximum and minimum recorded monomer temperatures is the temperature difference. And taking the date as a period of the data recorded according to a certain sampling frequency, and averaging to obtain the average value as a clustering variable.
The average value and the average speed value of the current of the Step1.2 battery are calculated and used as clustering variables in each day of use.
The K-means clustering in the Step2 comprises the following specific steps:
step2.1, selecting K initial central points as clustering centers;
determining the value of K by adopting an SSE (sum of the squared errors) method, wherein a specific algorithm is as follows:
Figure BDA0002297067560000041
wherein, ciIs the ith cluster, p is ciSample point of (1), miIs ciThe centroid of (1), SSE, is the clustering error of all samples, and represents how good the clustering effect is.
Step2.2, in the Nth iteration, calculating the distance from any sample to K centers, and classifying the sample into the class where the center closest to the sample is located;
Dklrepresents GkAnd GlThe sum of squared deviations formula is as follows:
Dki＝Wm-Wk-Wi
in the formula:
Figure BDA0002297067560000051
Figure BDA0002297067560000052
are respectively class GkClass GlAnd a center of gravity of the Gm-like.
Because data have different dimensions and different orders of magnitude, in order to make the data comparable, and make the data more equal to perform clustering analysis, it is necessary to perform standardized transformation on the data. Therefore, the raw data is converted into standard Z scores (Z scores) by adopting a standard normal transformation mode, and the calculation formula is as follows:
Figure BDA0002297067560000053
the squared euclidean distance is used, and the original data contains p variables, and each sample is a point in the p-dimensional space. By x ═ x1，x2，…，xp) And y ═ y1，y2，…，yp) Representing two samples, the squared euclidean distance between p variables of the two samples is calculated as follows:
Figure BDA0002297067560000054
step2.3, recalculating the average value of all the points in each cluster, and updating the average value to a new cluster center;
and Step2.4, repeating the processes of the second step and the third step until the cluster center does not change or is smaller than a given threshold value.
Application example 1: monthly running condition analysis of overall performance data of certain batch of batteries of electric vehicles
Patent pretreatment: the data preprocessing results of the change situation of the monitoring data of 100 battery modules of the power batteries of the electric automobile in a certain batch are shown in the following table:
Figure BDA0002297067560000055
Figure BDA0002297067560000061
the results of performing the K-means clustering analysis are shown in the following table, which shows only a part of the data because of the large amount of data.
Battery numbering Class cluster Distance
1 ① 20.569
5 ① 6.73
6 ① 5.54
12 ① 4.21
2 ② 7.61
4 ② 5.22
7 ② 2.69
11 ② 4.87
… … …
And (4) analyzing results: cluster 1 is the first type and comprises about 23 batteries. The 23 batteries which are gathered into one type are shown to have similar numerical values on 4 indexes of pressure difference, temperature difference, average current and average vehicle speed. Further observation shows that the pressure difference-temperature difference of the 23 batteries are mostly concentrated near the center of (0.15, 1) in two-dimensional coordinates. That is, the data attributed to the first type of battery set is characterized by a mean value of the differential pressure around 0.15, and a mean value of the differential pressure around 1. The control effect of the battery is better. Cluster 2 is a second category comprising about 49 batteries. In this divided second battery set, the center point of the current and voltage difference values is located at (2.8, 0.22). That is, the voltage difference and current values attributed to these batteries are characterized by a mean current value of about 2.8A, while the mean voltage difference is significantly greater than that of the first battery by about 0.22 v. It can be seen that the battery control effect of the second cluster is numerically worse than that of the first cluster. Cluster 3 is the third battery Cluster, which includes about 28 batteries. This type of battery data is characterized by a faster average speed, which means a higher average current. The center position of the average current-average speed was about (3.2, 13.1). The vehicle mounted with the battery is shown to run faster in the period of investigation.
In conclusion, the battery data classification and analysis system based on the k-means clustering method can conveniently and quickly find out the electrical performance data characteristics of the battery in operation, and further can help operation and maintenance personnel to quickly find out the battery problem characteristics, so that the reason can be searched purposefully.
The embodiment of the invention selects more important analysis indexes in the overall performance analysis of the battery: the differential pressure, the temperature difference, the average current and the average speed of the power battery are used as clustering variables, the association among data can be deeply excavated, and the classification of the battery data is better performed, so that the clustering result is more integrated, and the defects of the traditional method for analyzing the battery data by depending on experience are overcome.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (7)

1.一种基于聚类分析的电池故障分类诊断方法，其特征在于：包括以下步骤：1. a battery fault classification and diagnosis method based on cluster analysis, is characterized in that: comprise the following steps: S100、收集整理电池数据，将动力电池压差、温差、平均电流、平均车速作为聚类变量；S100. Collect and organize battery data, and use the power battery pressure difference, temperature difference, average current, and average vehicle speed as clustering variables; S200、利用K-means方法对电池数据进行聚类分析。S200, using the K-means method to perform cluster analysis on the battery data. 2.根据权利要求1所述的基于聚类分析的电池故障分类诊断方法，其特征在于：所述S100将动力电池压差、温差、平均电流、平均车速作为聚类变量；具体包括：2. The method for classifying and diagnosing battery faults based on cluster analysis according to claim 1, wherein the S100 uses the power battery voltage difference, temperature difference, average current, and average vehicle speed as cluster variables; specifically, it includes: S101、电池模组中记录的单体电压的最大值与最小值的差为压差，记录的单体温度的最大值与最小值的差为温差；再将这些按照一定采样频率记录的数据以日为一个周期，求平均值作为聚类变量；S101. The difference between the maximum value and the minimum value of the cell voltage recorded in the battery module is the pressure difference, and the difference between the maximum value and the minimum value of the recorded cell temperature is the temperature difference; then these data recorded according to a certain sampling frequency are The day is a cycle, and the average value is used as a clustering variable; S102、电池在使用的每日内，电流的平均值和平均速度值计算得出后作为聚类变量。S102 , in the daily use of the battery, the average value of the current and the average speed value are calculated and used as cluster variables. 3.根据权利要求1所述的基于聚类分析的电池故障分类诊断方法，其特征在于：所述S200利用K-means方法对电池数据进行聚类分析，具体包括：3. The method for classifying and diagnosing battery faults based on cluster analysis according to claim 1, wherein the S200 utilizes the K-means method to perform cluster analysis on battery data, specifically comprising: S201、选择K个初始中心点作为聚类中心；S201. Select K initial center points as cluster centers; S202、在第N次迭代中，对任意一个样本计算其到K个中心的距离，将该样本归到距离最近的中心所在的类；S202, in the Nth iteration, calculate the distance to K centers for any sample, and classify the sample into the class where the center with the closest distance is located; S203、重新计算每个聚类中所有点的平均值，并将其更新为新的聚类中心；S203, recalculate the average value of all points in each cluster, and update it as a new cluster center; S204、重复第二步、第三步的过程，直到聚类中心不再产生变化或小于给定的阈值。S204. Repeat the process of the second step and the third step until the cluster center no longer changes or is smaller than a given threshold. 4.根据权利要求3所述的基于聚类分析的电池故障分类诊断方法，其特征在于：所述S201选择K个初始中心点作为聚类中心；具体包括：4. The method for classifying and diagnosing battery faults based on cluster analysis according to claim 3, wherein the S201 selects K initial center points as cluster centers; specifically, it includes: 采用SSE的方法确定K的取值，具体算法如下：The SSE method is used to determine the value of K, and the specific algorithm is as follows:
Figure FDA0002297067550000011
Figure FDA0002297067550000011
 其中，ci是第i个簇，p是ci中的样本点，mi是ci的质心，SSE是所有样本的聚类误差，代表了聚类效果的好坏。Among them, ci is the ith cluster, p is the sample point in ci, m i is the centroid of ci , and SSE is the clustering error of all samples, which represents the quality of the clustering effect. 5.根据权利要求3所述的基于聚类分析的电池故障分类诊断方法，其特征在于：5. The battery fault classification and diagnosis method based on cluster analysis according to claim 3, is characterized in that: S202、在第N次迭代中，对任意一个样本计算其到K个中心的距离，将该样本归到距离最近的中心所在的类；具体包括：S202. In the Nth iteration, calculate the distance from any sample to K centers, and classify the sample into the class where the center with the closest distance is located; specifically, it includes: 设Dkl表示Gk和Gl之间的距离，则离差平方和法计算公式如下：Let D kl represent the distance between G k and G l , then the calculation formula of the squared deviation method is as follows: Dki＝Wm-Wk-Wi D ki =W m -W k -W i 式中：where:
Figure FDA0002297067550000021
Figure FDA0002297067550000021
Figure FDA0002297067550000024
分别是类Gk、类Gl和类Gm的重心。
Figure FDA0002297067550000024
are the centroids of class G k , class G l and class Gm, respectively. 6.根据权利要求5所述的基于聚类分析的电池故障分类诊断方法，其特征在于：所述步骤S202还包括：6. The method for classifying and diagnosing battery faults based on cluster analysis according to claim 5, wherein the step S202 further comprises: 采用标准正态变换方式，把原始数据转换为标准Z分数，其计算公式：The standard normal transformation method is used to convert the original data into standard Z-score. The calculation formula is as follows:
Figure FDA0002297067550000022
Figure FDA0002297067550000022
 采用的是平方欧式距离，原始数据中包含p个变量，那个每个样本就是p维空间中的一个点；The squared Euclidean distance is used, the original data contains p variables, and each sample is a point in the p-dimensional space; 用x＝(x1，x2，…，xp)和y＝(y1，y2，…，yp)表示两个样本，则两个样本p个变量之间的平方欧式距离计算公式如下：Using x=(x1, x2,..., xp) and y=(y1, y2,..., yp) to represent two samples, the square Euclidean distance between the p variables of the two samples is calculated as follows:
Figure FDA0002297067550000023
Figure FDA0002297067550000023
 7.根据权利要求1所述的基于聚类分析的电池故障分类诊断方法，其特征在于：所述S100收集整理电池数据，包括电池模组中反应单体差异的数据、电动车的使用情况数据、电池整体使用的性能数据。7 . The method for classifying and diagnosing battery faults based on cluster analysis according to claim 1 , wherein the S100 collects and sorts battery data, including data reflecting cell differences in the battery module and usage data of electric vehicles. 8 . , The performance data of the overall use of the battery.
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CN115782584A (en) * 2022-11-22 2023-03-14 重庆长安新能源汽车科技有限公司 New energy vehicle safety state determination method, system, equipment and medium
CN117007971A (en) * 2023-07-05 2023-11-07 湖南行必达网联科技有限公司 Battery fault diagnosis method, device and system

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