基于改进CNN和信息融合的动力电池组故障诊断方法*

doi:10.19562/j.chinasae.qcgc.2020.11.011

Abstract

Abstract: A fault diagnosis method for lithium-ion battery based on improved CNN and information fusion is proposed in this paper. Firstly, the Concat layer is added before the fully connected layer to fuse the features of different levels, so an improved CNN model is established. Then, the MCE criterion is used to optimize the cross entropy loss function, solving the problem that the non-label dimension gradient is not processed. In view of the insufficient practicality of the convolution kernel structure manually determined, the BIC criterion is used to determine the optimal structure of convolution kernel of the model. After diagnosis by using the improved CNN mentioned above, the conditions for confirmed diagnosis are introduced for judgment. For the diagnosis results not meeting the conditions, the general CNN network is further used for auxiliary diagnosis. By fusing preliminary diagnosis result and auxiliary diagnosis result using D-S evidence theory, the results of final diagnosis are obtained. The test results show that the method proposed can effectively enhance the accuracy of fault diagnosis on traction battery packs.

Key words: lithium-ion battery pack, fault diagnosis, convolutional neural network, BIC criterion, information fusion

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.

References

[1] 靳立强,孙志祥,刘志茹,等.不同温度下锂电池剩余电量估算的仿真研究[J]. 汽车工程,2019,41(5):590-598,606.
JIN L Q, SUN Z X, LIU Z R, et al. Simulation study on state of charge estimation of lithium-ion battery at different temperatures[J]. Automotive Engineering,2019, 41(5):590-598,606.
[2] 潘海鸿,吕治强,付兵,等.采用极限学习机实现锂离子电池健康状态在线估算[J].汽车工程,2017,39(12):1375-1381,1396.
PAN H H, LV Z Q, FU B, et al. Online estimation of lithium-ion battery’s state of health using extreme learning machine[J]. Automotive Engineering,2017,39(12):1375- 1381,1396.
[3] 檀斐.车用动力锂离子电池系统故障诊断研究与实现[D].北京:北京理工大学,2015.
TAN F. Fault diagnosis and implementation of electric vehicle lithium-ion battery system[D]. Beijing:Beijing Institute of Technology,2015.
[4] 古昂,张向文.基于RBF神经网络的动力电池故障诊断系统研究[J].电源技术,2016,40(10):1943-1945.
GU A, ZHANG X W. Fault diagnosis system for power battery based on RBF neural network[J]. Chinese Journal of Power Sources,2016,40(10):1943-1945.
[5] 夏飞,马茜,张浩,等.改进D-S证据理论在电动汽车锂电池故障诊断中的应用[J].智能系统学报,2017,12(4):526-537.
XIA F, MA X, ZHANG H, et al. Application of improved D-S evidence theory in fault diagnosis of lithium batteries in electric vehicles[J]. CAAI Transactions on Intelligent Systems,2017,12(4):526-537.
[6] 张倩,王建平,李帷韬.基于反馈机制的卷积神经网络绝缘子状态检测方法[J].电工技术学报,2019(16):3311-3321.
ZHANG Q, WANG J P, LI W T. Insulator state detection of convolutional neural networks based on feedback mechanism[J]. Transactions of China Electrotechnical Society,2019(16):3311-3321.
[7] 李恒,张氢,秦仙蓉,等.基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J].振动与冲击,2018,37(19):124-131.
LI H, ZHANG Q, QIN X R, et al. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network[J]. Journal of Vibration and Shock,2018,37(19):124-131.
[8] 曲建岭,余路,袁涛,等.基于一维卷积神经网络的滚动轴承自适应故障诊断算法[J].仪器仪表学报,2018,39(7):134- 143.
QU J L, YU L, YUAN T, et al. Adaptive fault diagnosis algorithm for rolling bearings based on one-dimensional convolutional neural network[J]. Chinese Journal of Scientific Instrument,2018,39(7):134-143.
[9] 王远.基于区域卷积神经网络的物体检测方法的研究与改进[D].哈尔滨:.哈尔滨工业大学,2017.
WANG Y. Research and improvement of object detection based on region based convolutional neural networks[D]. Harbin:Harbin Institute of Technology,2017.
[10] 陈文强,熊辉,李克强,等.基于深度神经网络的行人及骑车人联合检测[J].汽车工程,2018,40(6):726-732,725.
CHEN W Q, XIONG H, LI K Q, et al. Concurrent pedestrian and cyclist detection based on deep neural networks [J]. Automotive Engineering,2018,40(6):726 -732,725.
[11] 余烨,金强,傅云翔,等.基于Fg-CarNet的车辆型号精细分类研究[J].自动化学报,2018,44(10):1864-1875.
YU Y, JIN Q, FU Y X, et al. Fine-grained classification of car models using Fg-CarNet convolutional neural network[J]. Acta Automatica Sinica,2018,44(10):1864-1875.
[12] 郑宗生,侯倩,邹国良,等.基于改进最小分类误差准则算法的深度学习研究——以台风卫星云图为例[J].计算机应用研究,2019,36(10):3160-3163.
ZHENG Z S, HOU Q, ZOU G L, et al. Research on deep learning based on improved minimal classification error criterion algorithm: take typhoon satellite image as example[J]. Application Research of Computers,2019,36(10):3160-3163.
[13] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]. Proc. of IEEE Computer Vision and Pattern Recognition,2015:1-9.
[14] JUANG B H, KATAGIRI S. Discriminative learning for minimum error classification [pattern recognition][J]. IEEE Trans. on Signal Processing, 1992, 40 (12): 3043-3054.
[15] FENG Z Y, SUN Z H, JIN L W. Learning deep neural network using max-margin minimum classification error [C]. Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing,2016.
[16] 任超,吴伟,黄征凯,等.基于AIC准则的RBF神经网络在GPS高程拟合中的应用[J].测绘科学,2013,38(2):77-79.
REN C, WU W, HUANG Z K, et al. RBF neural network application in GPS height fitting based on AIC criterion[J]. Science of Surveying and Mapping,2013,38(2):77-79.
[17] 秦宣云,卜英勇,夏毅敏.基于AIC准则优化的径向神经网络微地形曲面重构[J].中南大学学报(自然科学版),2004(5):815-819.
QIN X Y, BU Y Y, XIA Y M. Surface reconstruction for micro-landform based on RBF neural network optimized by AIC criterion[J]. Journal of Central South University (Science and Technology),2004(5):815-819.
[18] 商云龙,张奇,崔纳新,等.基于AIC准则的锂离子电池变阶RC等效电路模型研究[J].电工技术学报,2015,30(17):55-62.
SHANG Y L, ZHANG Q, CUI N X, et al. Research on variable-order RC equivalent circuit model for lithium-ion battery based on the AIC criterion[J]. Transactions of China Electrotechnical Society,2015,30(17):55-62.
[19] 夏飞,袁博,彭道刚,等.基于信息量准则的锂离子电池变阶RC等效电路模型建模及优化方法[J].中国电机工程学报,2018,38(21):6441-6451,6506.
XIA F, YUAN B, PENG D G, et al. Modeling and optimization of variable-order RC equivalent circuit model for lithium-ion batteries based on information criterion[J]. Proceedings of the CSEE,2018,38(21):6441-6451,6506.
[20] 阮海斌.串联锂电池组均衡系统研究与设计[D]. 成都:西南交通大学,2017.
RUAN H B. Research and design of lithium-ion battery pack equalization system[D]. Chengdu:Southwest Jiaotong University,2017.

[1]	XU Cheng-Shan, JIANG Fa-Chao, SONG Sen-Nan, TIAN Guang-Yu. [J]. , 2017, 39(1): 9 -14 .
[2]	ZHANG Yi-Bing, 吕Jian-Jun , YUAN Guang-Hui. [J]. , 2016, 38(12): 1467 -1470 .
[3]	JIA Ji-De, WU Chun-Zhi, JIA Xiang-Yu, REN Gang, HAN Jia-Jia. [J]. , 2017, 39(1): 97 -101 .
[4]	RONG Bing, XIAO Pan, ZHOU Jian-Wen, LIU Yi-Lu. [J]. , 2017, 39(2): 214 -219 .
[5]	WANG Zhen, CUI Ya-Hui-.Liu-Kai, XU Lin. [J]. , 2017, 39(3): 304 -310 .
[6]	GUAN Xin, XU Liang, GUO Rui, WANG Bo, SONG Hai-Yan. [J]. , 2017, 39(3): 311 -316 .
[7]	JIN Geng-Ri, GUAN Xin, ZHAN Jun, GUO Rui. [J]. , 2017, 39(3): 364 -368 .
[8]	XIONG Xing-Wang, GAO Jun-Hua, ZHOU Tao, YU Jin-Tao. [J]. , 2017, 39(4): 381 -385 .
[9]	CAO Li-Feng, ZHOU Shou-Qin, XIE Xiao-Peng. [J]. , 2017, 39(4): 401 -406 .
[10]	Chen Wenqiang, Xiong Hui, Li Keqiang, Li Xiaofei& Zhang Dezhao. Concurrent Pedestrian and Cyclist Detection Based on Deep Neural Networks[J]. , 2018, 40(6): 726 .

Fault Diagnosis of Traction Battery Pack Based on Improved Convolution Neural Network and Information Fusion

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10

[1]	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.
[2]	Dongyu Zhao, Shuen Zhao. Autonomous Driving 3D Object Detection Based on Cascade YOLOv7 [J]. Automotive Engineering, 2023, 45(7): 1112-1122.
[3]	Lei Zhang, Keren Guan, Xiaolin Ding, Pengyu Guo, Zhenpo Wang, Fengchun Sun. Tire-Road Friction Estimation Method Based on Image Recognition and Dynamics Fusion [J]. Automotive Engineering, 2023, 45(7): 1222-1234.
[4]	Xia Zhao,Zhao Li,Rui Fu,Zhenzhen Ge,Chang Wang. Real-Time Detection of Distracted Driving Behavior Based on Deep Convolution-Tokens Dimensionality Reduction Optimized Visual Transformer Model [J]. Automotive Engineering, 2023, 45(6): 974-988.
[5]	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.
[6]	Yan Ma,Jiayi Li,Qian Ma,Mingchao Chen. Optimization Strategy of Thermal Management of Power Battery Pack Based on Iterative Dynamic Programming [J]. Automotive Engineering, 2022, 44(5): 709-721.
[7]	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.
[8]	Runze Feng,Kun Jiang,Weiguang Yu,Diange Yang. Chinese Traffic Sign Recognition Based on Two-stage Classification Algorithm [J]. Automotive Engineering, 2022, 44(3): 434-441.
[9]	Qiu Xia,Te Chen,Long Chen,Xing Xu,Yingfeng Cai. Vehicle Sideslip Angle Estimation Method Based on Redundant Information Fusion [J]. Automotive Engineering, 2022, 44(2): 280-289.
[10]	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.
[11]	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.
[12]	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.
[13]	Wang Hai, Wang Kuan, Cai Yingfeng, Liu Ze, Chen Long. Traffic Sign Recognition Based on Improved Cascade Convolution Neural Network [J]. Automotive Engineering, 2020, 42(9): 1256-1262.
[14]	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.
[15]	Dai Jinkun, Luo Yutao, Liang Weiqiang. Geometric Ranging of Unmanned Vehicle Driving Environment Image [J]. Automotive Engineering, 2020, 42(8): 1034-1039.