基于WOA-VMD和香农熵的锂电池早期故障诊断研究

doi:10.19562/j.chinasae.qcgc.2024.07.006

Abstract

Abstract:

A lithium battery early fault diagnosis method based on WOA-VMD and Shannon entropy is proposed in this paper to solve the problem of current battery management systems being unable to diagnose early faults. Firstly， the whale optimization algorithm is introduced to optimize the parameters of the variational mode decomposition algorithm to improve its decomposition performance and obtain intrinsic mode function components containing more fault feature information. Then， the voltage signal of the individual battery is decomposed and reconstructed to reduce the impact of measurement noise and additional excitation voltage. Furthermore， a sliding window is used to calculate the Shannon entropy range of individual voltage and the overall Shannon entropy of individual voltage dispersion to set appropriate thresholds for early fault diagnosis. After verification with actual vehicle data， this method can provide fault warning about 10 minutes in advance without generating false warnings for vehicles without faults. It has strong robustness and reliability.

Key words: whale optimization algorithm, variational mode decomposition algorithm, Shannon entropy, fault diagnosis

Jie Hu,Yayu Cheng,Hai Yu,Chaoming Jia,Haihua Qing. Research on Early Fault Diagnosis of Lithium Battery Based on WOA-VMD and Shannon Entropy[J].Automotive Engineering, 2024, 46(7): 1189-1196.

Figures/Tables 14

香农熵极差 $S E i d e v$	总体香农熵 $S E i t o t a l$	诊断结果
$S E i d e v < 2$	$S E i t o t a l < 4.7$	正常
$S E i d e v < 2$	$S E i t o t a l > 4.7$	一级预警
$S E i d e v > 2$	$S E i t o t a l < 4.7$	一级预警
$S E i d e v > 2$	$S E i t o t a l > 4.7$	二级预警

References 15

1	武美君.基于数据驱动与有向图模型的动力电池系统故障诊断研究［D］. 武汉：武汉理工大学，2021.
	WU M J. Research on fault diagnosis of power battery system based on data-driven and directed graph models ［D］. Wuhan： Wuhan University of Technology，2021.
2	刘晓俊. 电池故障智能诊断系统的研究与实现［D］. 北京：北京邮电大学，2011.
	LIU X J. Research and application of intelligent battery fault diagnosis system ［D］. Beijing： Beijing University of Posts and Telecommunications， 2011.
3	马宗钰，兰海潮，宋若旸，等. 基于支持向量机的动力电池故障诊断［J］. 汽车实用技术， 2021， 46（2）： 5-7.
	MA Z Y， LAN H C， SONG R Y， et al. Power battery fault diagnosis based on Support Vector Machine ［J］. Practical Automotive Technology， 2021， 46 （2）： 5-7.
4	MARCICKI J， ONORI S， RIZZONI G. Nonlinear fault detection and isolation for a lithium-ion battery management system［C］.ASME 2010 Dynamic Systems and Control Conference， Volume 1. Cambridge， Massachusetts， USA： ASMEDC， 2010： 607-614.
5	MASRI B A， SHEIKH H A， MOUBAYED N. Sensor fault detection of lithium-ion batteries based on extended Kalman filter［C］.2020 International Conference on Electrical， Communication， and Computer Engineering （ICECCE）. Istanbul， Turkey： IEEE， 2020： 1-5.
6	曾港辉. 锂离子电池微过充电化学-热耦合模拟与安全预警研究［D］. 长沙：湖南大学， 2022.
	ZENG G H. Electrochemical thermal coupling simulation and safety warning for slight overcharge of lithium-ion battery ［D］. Changsha：Hunan University， 2022.
7	SHANG Y L， LU G P， KANG Y Z， et al. A multi-fault diagnosis method based on modified sample entropy for lithium-ion battery strings［J］. Journal of Power Sources， 2020， 446： 227275.
8	YAO L， XIAO Y Q， GONG X Y， et al. A novel intelligent method for fault diagnosis of electric vehicle battery system based on wavelet neural network［J］. Journal of Power Sources， 2020， 453： 227870.
9	YAO L， FANG Z P， XIAO Y Q， et al. An intelligent fault diagnosis method for lithium battery systems based on grid search support vector machine［J］. Energy， 2021， 214： 118866.
10	HONG J C， WANG Z P， YAO Y T. Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks［J］. Applied Energy， 2019， 251： 113381.
11	WANG J， ZHANG S L， HU X. A fault diagnosis method for lithium-ion battery packs using improved RBF neural network［J］. Frontiers in Energy Research， 2021， 9： 702139.
12	LI D， ZHANG Z S， LIU P， et al. DBSCAN-based thermal runaway diagnosis of battery systems for electric vehicles［J］. Energies， 2019， 12（15）： 2977.
13	LI X Y， DAI K W， WANG Z P， et al. Lithium-ion batteries fault diagnostic for electric vehicles using sample entropy analysis method［J］. Journal of Energy Storage， 2020， 27： 101121.
14	DRAGOMIRETSKIY K， ZOSSO D. Variational mode decomposition［J］. IEEE Transactions on Signal Processing， 2014， 62（3）： 531-544.
15	MIRJALILI S， LEWIS A. The whale optimization algorithm［J］. Advances in Engineering Software， 2016， 95： 51-67.

数据字段名称	数据说明
total_volt	总电压/V
total_current	总电流/A
cell_volt_list	单体电池电压值列表
cell_temp_list	单体电池温度值列表
max_cell_volt	单体电池电压最高值/V
min_cell_volt	单体电池电压最低值/V
alarm_info	通用报警标志
max_alarm_lvl	最高报警等级

故障车辆	故障类型	预警时间
1号车	单体电池欠压	8 min 56 s
2号车	单体电池过压	11 min 02 s
6号车	车载储能装置过压	9 min 23 s
7号车	单体电池欠压、电池单体一致性差	8 min 24 s

[1]	Jiqing Chen,Yujia Feng,Fengchong Lan,Ping Wang. Fault Diagnosis Method for Power Battery Based on Quantification of Cell Abnormality with 1dCNN-LSTM [J]. Automotive Engineering, 2024, 46(7): 1177-1188.
[2]	Jianping Wang,Jian Ma,Dean Meng,Xuan Zhao,Qi Bian,Kai Zhang,Qiquan Liu. Fault Diagnosis of Automotive PMSMs Based on EMD-SDP Image Features and Improved DenseNet [J]. Automotive Engineering, 2024, 46(4): 703-716.
[3]	Pengbo Zhang, Renxiang Chen, Yiming Shao, Shizheng Sun, Kaibo Yan. Research Review of Fault Diagnosis for Electric Drive Powertrain System of Pure Electric Vehicles [J]. Automotive Engineering, 2024, 46(1): 61-74.
[4]	Jichao Hong,Fengwei Liang,Haixu Yang,Kerui Li. Research on Intelligent Safety Management and Control Methods for Big-data-driven Battery Systems [J]. Automotive Engineering, 2023, 45(10): 1845-1861.
[5]	Qixiang Zhang,Liqiang Jin,Bohao Jin,Yihan Zhang,Pengfei Chen,Yongteng Liu,Jianhua Li. Research Progress of EMB Clamping Force Control and Sensor Fault Diagnosis [J]. Automotive Engineering, 2022, 44(5): 736-746.
[6]	Hai Yu,Junjun Deng,Zhenpo Wang,Fengchun Sun. Inverter Fault Diagnosis Method Based on Convolutional Neural Network [J]. Automotive Engineering, 2022, 44(1): 142-152.
[7]	Ying Jin,Xinyong Qiao,Cheng Gu,Hao Guo,Chuming Ning. A Method for Fault Diagnosis of Fuel Injector of Diesel Engine Based on Res⁃CNN and Fuel Pressure Wave [J]. Automotive Engineering, 2021, 43(6): 943-951.
[8]	Li Zhang,Xingqi Gao,Jianhao Zhang. Short-circuit Current Estimation of Battery Pack Based on Average- difference Model [J]. Automotive Engineering, 2021, 43(11): 1702-1709.
[9]	Zhu Shaopeng, Fu Qitao, Huang Xiaoyan, Wang Zhiwei, Yang Xinghao. Motor Fault Diagnosis and Failure Control for Distributed Four-wheel Drive Electric Vehicles [J]. Automotive Engineering, 2020, 42(9): 1284-1291.
[10]	Qiao Xinyong, Gu Cheng, Han Lijun. Diesel Engine Fault Diagnosis Method Based on VMD and Multi-Scale Dispersion Entropy [J]. Automotive Engineering, 2020, 42(8): 1139-1144.
[11]	Zhang Yangyang, Jia Yunxian, Wu Weiyi, Su Xiaobo, Shi Xiaowen. Application of Probabilistic Neural Network to Typical Fault Diagnosis of Vehicle Gearbox [J]. Automotive Engineering, 2020, 42(7): 972-977.
[12]	Peng Yunsai, Xia Fei, Yuan Bo, Wang Zhicheng, Luo Zhijiang. Fault Diagnosis of Traction Battery Pack Based on Improved Convolution Neural Network and Information Fusion [J]. Automotive Engineering, 2020, 42(11): 1529-1536.
[13]	Zhao Zhiguo, Fu Shihao, Jiang Siwen, Chen Jiayi. Fault Diagnosis of Position Sensor for Synchronizer Actuator Motor in Dry Dual Clutch Transmission [J]. Automotive Engineering, 2020, 42(11): 1585-1594.
[14]	Chen Qi, Wang Jincheng, Qadeer Ahmed, Yao Zhigang , Chen Wuwei. Model-based Fault Diagnosis of Automotive Electric Power Steering System [J]. Automotive Engineering, 2019, 41(7): 839-850.
[15]	Zhou Xiaolong, Liu Weina, Jiang Zhenhai, Ma Fenglei. Application of Volterra Mode of Variational Mode Decomposition and Morphology Fractal Dimension in Engine Fault Diagnosis [J]. Automotive Engineering, 2019, 41(12): 1442-1449.

Research on Early Fault Diagnosis of Lithium Battery Based on WOA-VMD and Shannon Entropy

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 15

Related Articles 15

Metrics

Comments

Recommended 0