[1]XIA Fei,MA Xi,ZHANG Hao,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.[doi:10.11992/tis.201605001]
Copy

Application of improved D-S evidence theory in fault diagnosis of lithium batteries in electric vehicles

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 4 Page number: 526-537 Column: 学术论文—智能系统 Public date: 2017-08-25
Title:
Application of improved D-S evidence theory in fault diagnosis of lithium batteries in electric vehicles
Author(s):
XIA Fei1; 2; 3;  MA Xi1; 2;  ZHANG Hao1; 2; 3;  PENG Daogang1; 2;  SUN Peng1; 2;  LUO Zhijiang1; 2
1. College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
2. Shanghai Engineering Research Center of Intelligent Management and Control for Power Process, Shanghai 200090, China;
3. College of Electronics a
Keywords:
fault diagnosis; electric vehicle; lithium battery; improved evidence theory; information fusion
CLC:
TP301
DOI:
10.11992/tis.201605001
Abstract:
In this study,we used the improved Dempster-Shafer (D-S) evidence theory combination rules based on the neural network to construct a fault diagnosis process for an electric vehicle battery system.To avoid misdiagnoses and missed diagnoses caused by a single fault diagnosis method,we applied the D-S evidence theory combination principle to determine the result based on the back-propagation (BP) network and radial basis function (RBF) network fault diagnosis algorithm.However,to overcome the defects in the D-S evidence theory in dealing with highly conflicting evidence,we propose a D-S evidence theory combination principle based on an improved neural network.First,we apply a neural network to perform a preliminary diagnosis regarding battery failure and the accuracy of the network diagnosis.Then,we distribute indefinite information and construct a body of evidence.We also introduce a support matrix of this evidence to determine a new weighted body of evidence.We then integrate the credibility of every focal element into the D-S evidence theory combination rules to fuse the neural network body of evidence with the new weighted body of evidence.Lastly,based on the decision criterion,we determine the failure state of the lithium battery system.Our simulation results show that our proposed improved D-S evidence theory fusion diagnosis method is effective in the fault diagnosis of electric vehicles with lithium batteries.
References:
[1] 王一卉,姜长泓.模糊神经网络专家系统在动力锂电池组故障诊断中的应用[J]. 电测与仪表, 2015, 52(14):118-123. WANG Yihui, JIANG Changhong. Fuzzy neural network ex-pert system for fault diagnosis in power lithium battery appli-cation[J]. Electrical measurement & instrumentation, 2015, 52(14):118-123.
[2] 卿平勇.混合动力汽车电池管理系统故障诊断与健康管理研究[D].北京:北京理工大学,2015. QING Pingyong. Reserch on fault diagnosis and health man-agement of battery management system for hybrid electric ve-hicle[D].Beijing:Beijing Institute of Technology,2015.
[3] 檀斐.车用动力锂离子电池故障诊断研究与实现[D].北京:北京理工大,2015. TAN Fei. Fault diagnosis and implementation of electric ve-hicle lithiumion battery system[D].Beijing:Beijing Institute of Technology,2015.
[4] 付家才,万遂.基于D-S证据理论和BP神经网络的多传感器信息融合[J].自动化与仪器仪表, 2011, 1(153):22-24. FUJiacai,WAN Sui. Multisensor information fusion based on D-S evidence theory and BP neural network[J]. Automation & instrumentation, 2011, 1(153):22-24.
[5] 程加堂,段志梅.基于QPSO-BP和改进D-S的水电机组振动故障诊断[J].电力系统保护与控制, 2015,43(19):66-71. CHENG Jiatang, DUAN Zhimei, AI Li, et al. Vibration fault diagnosis for hydroelectric generating unit based on QPSO-BP and modified D-S theory[J]. Power system pro-tection and control, 2015, 43(19):66-71.
[6] 徐春梅,彭道刚,张悦. 基于集成法的汽轮机组智能故障诊断仿真研究[J]. 计算机仿真, 2015(07):408-412. XU Chunmei,PENG Daogang,ZHANG Yue. simulation re-search on intelligent fault diagnosis for turbine generator unit based on integrated method[J]. Computer simulation, 2015(07):408-412.
[7] 白剑林,王煜.一种解决D-S理论证据冲突的有效方法[J].系统工程与电子技术,2009,31(9):2106-2109. BAI Jianlin, WANG Yu. Efficient combination approach to conflict evidence for D-S theory[J]. Systems engineering and electronics, 2009,31(9):2106-2109.
[8] 韩德强,邓勇,韩崇昭,等. 基于证据距离与不确定度的证据组合方法[J]. 红外与毫米波学报, 2011, 30(5):396-400. HAN Deqiang, DENG Yong, HAN Chongzhao, et al. Weighted evidence combination based on distance of evidence and uncertainty measure[J]. Journal of infrared millim waves, 2011, 30(5):396-400.
[9] 郭雷雷.智能环境下基于视频多特征融合的单说话人跟踪方法研究[D].兰州:兰州理工大学,2014. GUO Leilei. The research of single speaker tracking algorithm based on video multi-feature fusion in meeting room environment[D].Lanzhou:Lanzhou university of tech-nology, 2014.
[10] 牛强军,黄家成.基于改进的D-S决策融合的航电设备故障诊断[J]. 计算机工程与设计, 2015, 36(8):2255-2259. NIU Qiangjun, HUANG Jiacheng. Avionics equipment fault diagnosis based onimproved dempster-shafe decision fusion method[J]. Computer engineering and design, 2015, 36(8):2255-2259.
[11] Mengmeng Ma, Jiyao An.Combination of evidence with dif-ferent weighting factors:a novel probabilistic-based dissim-ilarity measure approach[J]. Journal of sensors, 2015:1-9.
[12] 费翔,周健.一种处理冲突证据的D-S证据权重计算方法[J]. 计算机工程, 2016, 42(2):142-145. FEI Xiang, ZHOU Jian. A D-S evidence weight computing method for conflict evidence[J]. Computer engineering, 2016, 42(2):142-145.
[13] 胡海亮,钟求喜.基于证据可信度的D-S理论改进方法[J].计算机应用与软件, 2016, 33(6):13-19. HU Hailiang, ZHONG Qiuxi. An improved method for d-s theory based on evidence credibility[J]. Computer applica-tions and software, 2016, 33(6):13-19.
[14] RONALD R Y. On the dempster-shafer framework and new combination rules[J]. Information sciences (S1007-7634), 1987, 41(2):93-137.
[15] 孙全,叶秀清,顾伟康. 一种新的基于证据理论的合成公式[J]. 电子学报, 2000(08):117-119. SUN Quan, YE Xiuqing, GU Weikang. A new combination rules of evidence theory[J]. Acta electronica sinica, 2000(08):117-119.
[16] 李弼程, 王波, 魏俊,等. 一种有效的证据理论合成公式[J]. 数据采集与处理, 2002, 17(1):33-36. LI Bicheng, WANG Bo, WEI Jun,et al. An efficient com-bination rule of evidence theory[J]. Journal of data acqui-sition & processing, 2002, 17(1):33-36.
[17] 王力.基于DS证据理论的多传感器数据融合算法研究与应用[D].太原:太原理工大学,2015. WANG li. Research and application of multi-sensor fusion algorithm based on DS evidence theory[D]. Taiyuan:Taiyuan University of Technology, 2015.
[18] ZHAO Y, JIA R, LIU C. An evaluation method of under-water ocean environment safety situation based on d-s evi-dence theory[J]. Advances in meteorology, 2015, 2015(5):1-8.
[19] 吴强,姜礼平,季傲.基于模糊集和D-S证据理论的空中作战目标识别[J].指挥控制与仿真, 2015, 36(4):54-58. WU Qiang, JIANG Liping, JI Ao. Aircraft target identification based on fuzzy sets and d-s evidence theory in air operation[J]. Command control & simulation, 2015, 36(4):54-58.
[20] 吕雪婷,贾瑞生, 孙惠惠.证据冲突下D-S融合算法的改进[J].系统仿真学报,2013, 25(3):571-574. LV Xueting, JIA Ruisheng, SUN Huihui. Improvement of D-S fusion algorithm under evidence conflict[J]. Journal of system simulation, 2013, 25(3):571-574.
[21] 叶彦斐,郑源.基于证据修正及冲突分配的新证据推理规则[J].仪表技术与传感器, 2014, 12:118-121. YE Yanfei, ZHENG Yuan. New evidence reasoning rule based on eveidence modifying and conficts allocating[J]. Instrument technique and sensor, 2014, 12:118-121.
[22] 曹洁,郭雷雷.一种基于局部冲突分配的证据组合规则[J].计算机应用研究, 2013, 30(7):2032-2035. CAO Jie, GUO Leilei. Evidence combination rule based on local conflict distribution strategy[J]. Application research of computers, 2013, 30(7):2032-2035.
[23] 李军伟,程咏梅, 潘泉,等.基于焦元距离的冲突证据组合规则[J].系统工程与电子技术, 2010, 32(11):2360-2366. LI Junwei, CHENG Yongmei, PAN Quan, et al. Combina-tion rule of conflicting evidence based on focal element dis-tance[J]. Systems engineering and electronics, 2010, 32(11):2360-2366.
[24] DUBOIS D,PRADE H. A set-theoretic view on belief func-tions:logic operations and approximations by fuzzy sets[J]. International journal of general syst, 1986, 12:193-226.
[25] 葛哲学,孙志强.神经网络理论与MATLABR2007实现[M]. 北京:电子工业出版社,2007:240.
Similar References:

Memo

-

Last Update: 2017-08-25

Copyright © CAAI Transactions on Intelligent Systems