Electric Vehicle Battery Fault Diagnosis Based on ... - ScienceDirect

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ScienceDirect Energy Procedia 105 (2017) 2366 – 2371

The 8th International Conference on Applied Energy – ICAE2016

Electric vehicle battery fault diagnosis based on statistical method Yang Zhaoa,b, Peng Liua,b,*, Zhenpo Wanga,b, Jichao Honga,b aNational Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, 100081, China bBeijing Co-innovation Center for Electric Vehicles Lecturer, Beijing, 100081, China

Abstract Fault diagnosis of battery power system can clear the fault type, locate the fault location, avoid the failure, and it has very positive effect to increase the stability of electric cars. According to the statistical analysis of electric car big data, this paper researches the evolution regulation and abnormal changes of battery voltage, which accordingly determine the probability of battery fault. Finally, corresponding to the actual vehicle, the statistical fault diagnosis conclusions convert into actual vehicle fault diagnosis conclusions. According to the statistical analysis methods of big data, this paper applies 3σ multi-level screening fault diagnosis which based on Gaussian distribution on determining the fault probability of the battery cell terminal voltage. For the fault statistical analysis of large numbers of electric cars, neural network is used to model big sample statistical law and fit. Applying the neural network algorithm, this paper combines the single car’s fault diagnosis results with big sample statistical regulation, construct a more complete battery system fault diagnosis method, and make a corresponding analysis between the statistical result and actual vehicle. © 2017 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy.

Keywords: Electric vehicle; Power battery system; Fault diagnosis; Big data statistics; Neural network.

1. Introduction With the development of electric vehicle industry, the safety and quality of electric cars become the focus of people’s attention. As a key part of electric cars, battery power system directly influences the performance of electric vehicles. However, the increasing risks of battery power system failure occurred in recent years make consumers who have electric cars very worried, so accelerating researches on fault diagnosis and safety management of electric vehicle battery system becomes particularly important [1].From the viewpoint of German professor Frank, we can know that the fault diagnosis method is divided into three kinds, they are based on analytic model, signal processing method and knowledge based on reasoning method [2]. Nowadays, scholars all over the world are studying the fault diagnosis of battery system. Oliver Bohlen through the battery on-line diagnosis system model to identify the impedance of the The project was supported by the Joint Funds of the National Natural Science Foundation of China (No. U1564206). * Corresponding author. Tel.: 010-59419650. E-mail address: [email protected].

1876-6102 © 2017 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.679

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Yang Zhao et al. / Energy Procedia 105 (2017) 2366 – 2371

battery [3]. Zheng Y used the least squares method and entropy for off-line analysis of the data to get the internal resistance identification [4]. As Grobelink M said, in today's management and decision-making in the leader is no longer too much relying on experience and intuition, but more scientific to rely on data analysis to make decisions [5]. The statistical method has been applied to the fault diagnosis. Xuelin Lu applied multivariate statistical methods at fault diagnosis of servo drive system [6]. In this paper, statistical analysis is used to determine the battery fault. The fault diagnosis method based on statistical analysis is more efficient and accurate, and can predict the occurrence of faults in advance. According to the statistical analysis methods of big data, this paper applies 3σ multi-level screening fault diagnosis which based on Gaussian distribution on determining the fault probability of the battery cell terminal voltage. Applying the neural network algorithm, this paper combines the single car’s fault diagnosis results with big sample statistical regulation, construct a more complete battery system fault diagnosis method, and make a corresponding analysis between the statistical result and actual vehicle. 2. Statistical model 2.1. Process flow diagram of the model This paper obtains the data from Beijing Electric vehicles monitoring and service center. According to the data format and statistical algorithms, we set up the statistical model of cells terminal voltage faults diagnosis. We establish the process flow diagram of the model, as illustrated in Fig.1. START

Record the position overranging 3σ2 interval in matrix R2.

Import data from Beijing Electric Vehicles Monitoring and Service Center.

δ=σ1-σ2

Screening and Sorting operitions on data to bulid data matrix D.

δ>specified value;

Calculate the size of D and return m rows, n cols. YES

Whether to loop through m rows ?

YES

NO

Output μ2ͫ σ2ͫ and establish X~N2(μ2ͫ σ2)

μ1=μ2; σ1=σ2; R1=R2;

NO Sum the columns of R2,and get the fault frequency of each cell in matrix S2.

Calculate the mean(μ1) and standard deviation(σ1)

Establish Gaussian distribution X~N1(μ1,σ1)

Whether there is a high fault frequency of some cells ;

YES NO Calculate the position overranging 3σ1 interval and record the location(i,j) in fault matrix R1.

Remove the fault data recorded in matrix R1,and recalculate the mean(μ2),std(σ2)澞

Output the fault position in battery pack.

Analyze and get the statistical regulation of fault distribution

END

Fig. 1. Process flow diagram of battery fault statistical model

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2.2. Electric vehicle data source The real-time running data of electric vehicle mainly includes power battery system, motor drive system, vehicle control system, and some other electrical parts. The data of the power battery system mainly includes: the total voltage of the battery system and the total current, the SOC state, the cell voltage, the battery pack characteristic point temperature. Motor drive system data includes: voltage, current, speed, torque, temperature, etc. The vehicle control system data mainly includes: vehicle speed, gear position, accelerator pedal value, brake pedal value, system insulation condition, motor fault condition, battery fault condition, vehicle system fault condition, etc. Other parts of the data mainly include: air conditioning, tire pressure state, etc. The terminal voltage data format we obtained from the monitoring center is a matrix. The row vectors of the matrix consist of cells voltage at the same time. Only one type of vehicle is studied in this paper. There are 91 cells in vehicle’s battery pack, so the matrix has 91 columns. The time interval of data recording between the rows is 10s. 2.3. Abnormal diagnosis Statistical model Most of the traditional fault analysis process is to find fault first, then to find the reasons, finally to find the functional relation between the reasons and the fault. However, the coupling between parameters is complicated, and the difficulty of modeling has direct impact on the accuracy and timeliness of data processing.

f x

§ ( x P )2 · 1 exp ¨ ¸ 2 2SV © 2V ¹

(1)

Using Gaussian distribution law and 3³confidence criterion method, find out the law of data, and do the data comparison and calibration, can quickly realize the fault and the confirmation of the diagnosis. In this paper, the distribution of cells terminal voltages of the same battery pack is described by Gaussian distribution. Due to the temperature stress inconsistency inside the battery power system, the battery degradation along with it is also inconsistent and the voltage will be according to a certain temperature field stress rules formed certain difference in deterioration and change with time t, as a relatively stable statistical rules: U t ( Pt , V t ) (2) 3σ multi-level screening strategy is using Gaussian distribution probability characteristics for fault-free data centralized screening, after several rounds of screening identified the nearest parameters of ܷ௧ . 3σ multi-level screening strategy has the advantages: this screening can also remove all more than 3σ confidence interval data, processing efficiency is high; the method in accordance with the confidence interval to adjust the threshold. It provides an efficient algorithm for the data processing of the number of vehicles, vehicle types, and different time. In the latter part of the operation is greatly saved the time, improve the efficiency of calculating. 1˅Basic process of the strategy illustrated as following: According to the real time data of electric vehicle, construct the matrix of the original data of n cells voltage at the time t˖ Ut ,0 (U1,0 U n,0 ) (3) And carries on the variable calculation, gets: U t ,0 ( Pt 0 , V t 0 ) (4) 2˅Remove the data outside the 3σ range, and construct a new matrix containing n1 voltage data:

Ut ,1 (U1,1 U n1,1 ) And carries on the variable calculation, gets:

Ut ,1 (Pt1 , V t1 )

(5) (6)

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3˅Repeat the (2) step on data screening process more than m times, in which m t 2 ; Get the final Gaussian distribution: Ut ,m (Ptm , V tm ) (7) 4˅According to the values Ptm V tm and Ptm V tm , compare the data in the matrix. Out of the range returns 1ˈwithin the range returns 0ˈconstruct the frequency matrix beyond the threshold of voltage data at the time t˖ M t (M t ,1 M t ,n ) (8) In which, M t ,i = 0 or 1; 5˅For the data in a certain period of (t0 ~ t1 ) , carries on the cycle calculation from step (1) to step (4) to gets the frequency matrix beyond the threshold in this period of time:

M

t1

¦M

(9)

t

t0

3. Diagnosis of fault statistical distribution 3.1. Two types of cells fault A big quantity data from different vehicles which have the same type are collected. Through big sample statistical analysis of these vehicles, we define two fault types of cells. 1) For a small part of vehicles, the cells voltages of which often exceed the 3σ interval, whose frequency is more than 90%, and the location of faults are not fixed, as shown in the Fig. 2. (top left). This kind of fault could be caused by production problems or some accidents. 2) For a big part of vehicles, the frequency of which is below 35%, and the location of faults is fixed, as shown in Fig. 2. (Top right, bottom left, bottom right). This kind of fault could be caused by vehicle designing defects or some inherent problems.

Fig. 2. Two types fault comparison

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3.2. Statistical analysis The BP neural network model built in this paper has 3 layers. The neural network is used to fit the distribution of second type of fault. After a lot of training by big sample, the statistical regulation of the second fault type is shown in figure3. From the figure, the battery cells voltage whose position near the number12, 40, 60have significant abnormal changes. This will be checked corresponding to the actual vehicle in the next section.

Fig. 3. The output of the neural network after massive training

8-110

12-16

35-39

40-44

56-58

5-7

17-19

30-31

45-46

53-55

1-4

20-24

25-29

47-49

50-52

59-91

Fig. 4. Battery cells layout diagram

3.3. Statistical results and actual vehicle verification The battery layout of the vehicle researched in this paper is shown as figure 4. From this figure, cells numbered from 1 to 58 are of the horizontal arrangement, and cells numbered from 59 to 91 are of the vertical arrangement. Number 1 is on the top, and number4 is at the bottom; number5 is at the bottom, and number7 is on the top; cell number58 is close to the number59; in the right of the battery pack, number59 to 91 are sequentially arranged from up to down. Because the figure above reading is not intuitive, where we use CATIA to do a three-dimensional description, 3D cell layout as shown in Figure 5. Each point in the graph represents a cell, and each arrow represents the direction of the cells connection. According to the conclusion of the last section, we mark the cells position with the number 12, 40, 59in


red circles. We find that the areas nearby the number 12 and 40 is on the forward and bottom of the battery pack. The areas nearby number 59 is the converted place of vertical arrangement and horizontal arrangement of cells.

Fig. 5. The position of the fault in actual vehicle

4. Conclusion This paper mainly through the statistical algorithms for the battery fault diagnosis and build the Gaussian distribution of the battery cells terminal voltage produced at the same time, and the abnormity degree of terminal voltage is illustrated by the probability characteristics of Gaussian distribution. Modeling the fault analysis of power battery system using data statistics. Through 3σ multi-level screening strategy, the abnormal data efficiently and accurately removed, and can adjust the screening threshold. Two fault types about abnormal voltage are proposed: First, the fault produced due to some production problems or some accidents, and the position of which is random. Second, the fault produced by some designing problems or some other inherent problems, and the position of which is fixed. Using neural network to model the big sample fault statistics, this paper establishes the [1, 10, 10, 1] structure BP neural network, and makes the fitting of the big sample fault regulation. According to the statistical analysis of second types of fault, distribution is obtained and corresponding to the space position of the real vehicle. We get the conclusion that, under the very large probability, the performance of front and bottom cells in the battery pack will drop more quickly. References [1] Wagner F T, Lakshmanan B and Mathias M F. Electrochemistry and the future of the automobile [J]. Joiurnal of Physics Chemical Letters, 2010, V1: 2204-2219. [2] Frank P M. Fault diagnosis in dynamics systems using analytical and knowledge-based redundancy: a survey and some new results [J]. Automatica, 1990, 26 (3): 459-474. [3] Bohlen O, Buller S, De Doncker R W, et al. Impedance based battery diagnosis for automotive applications[C]. Power Electronics Specialists Conference, 2004.PESC 04. 2004 IEEE 35th Annual. IEEE, 2004, 4: 2792-2797. [4] Zheng Y, Han X, Lu L, et al. Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles[J]. Journal of Power Sources, 2013, Vl (223): 136-146. [5] Grobelink MˊBig- data computing: Creating revolutionary breakthroughs in commerceˈscience and society [N/OL].201210-02. [6] Lu Xue-lin. Application of higher-order statistic to servo drive system fault diagnosis [J], 2008.V30:84-90.

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