Challenge and Prospects for Fault Diagnosis of Power Battery System for Electrical Vehicles Based on Big-data

doi:10.3901/JME.2021.14.052

Abstract

Abstract: Battery fault diagnosis techniques are regarded as significant means for guaranteeing safe operation of electric vehicles (EVs). Precise and effective techniques not only can improve safety and reliability of EVs but also accelerate the progress of EVs' market. Firstly, focusing on the battery management system and thermal management system, the latest research progress of battery state estimation and cooling technology in ensuring the safe operation of EVs is reviewed; Secondly, advanced technical means of data transmission security of battery system operation are introduced respectively at the vehicle local level and the vehicle terminal cloud network level; Additionally, from the perspective of big data, the fault diagnosis technology is summarized into three aspects:multi-scale data fusion, fault identification, and fault pre-alarming, and the advantages and disadvantages of the current technology are analyzed; Finally, in view of the difficulties faced by current fault diagnosis technology, the future research trend of EV's fault diagnosis method combining big data and artificial intelligence technology is prospected.

Key words: big data, battery system, fault diagnosis, safety management

CLC Number:

TG156

WANG Zhenpo, LI Xiaoyu, YUAN Changgui, LI Xiaohui. Challenge and Prospects for Fault Diagnosis of Power Battery System for Electrical Vehicles Based on Big-data[J]. Journal of Mechanical Engineering, 2021, 57(14): 52-63.

References

[1] 万钢. 新时代推进我国新能源汽车发展的新思考[J]. 汽车工程学报,2018,8(4):235-238. WAN Gang. New thoughts on promoting the development of China's new energy vehicles in the new era[J]. Chinese Journal of Automotive Engineering,2018,8(4):235-8.
[2] ABADA S,MARLAIR G,LECOCQ A,et al. Safety focused modeling of lithium-ion batteries:A review[J]. Journal of Power Sources,2016,306:178-192.
[3] DUAN J,TANG X,DAI H,et al. Building safe lithium-ion batteries for electric vehicles:A review[J]. Electrochemical Energy Reviews,2020,3(1):1-42.
[4] DENG J,BAE C,MARCICKI J,et al. Safety modelling and testing of lithium-ion batteries in electrified vehicles[J]. Nature Energy,2018,3(4):261-6.
[5] 冯旭宁. 车用锂离子动力电池热失控诱发与扩展机理,建模与防控[D]. 北京:清华大学,2016. FENG Xuning. Thermal runaway initiation and propagation of lithium-ion traction battery for electric vehicle:Test,modeling and prevention[D]. Beijing:Tsinghua University,2016.
[6] 陈吉清,刘蒙蒙,周云郊,等. 不同滥用条件下车用锂电池安全性实验研究[J]. 汽车工程,2020(1):66-73. CHEN Jiqing,LIU Mengmeng,ZHOU Yunjiao,et al. Experimental study on safety of automotive NCM battery under different abuse conditions[J]. Automotive Engineering,2020(1):66-73.
[7] LI X,DAI K,WANG Z,et al. Lithium-ion batteries fault diagnostic for electric vehicles using sample entropy analysis method[J]. Journal of Energy Storage,2020,27:101121.
[8] WANG X,XIE Y,DAY R,et al. Performance analysis of a novel thermal management system with composite phase change material for a lithium-ion battery pack[J]. Energy,2018,156:154-168.
[9] HU X,FENG F,LIU K,et al. State estimation for advanced battery management:Key challenges and future trends[J]. Renewable and Sustainable Energy Reviews,2019,114:109334.
[10] WEI J,DONG G,CHEN Z. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression[J]. IEEE Transactions on Industrial Electronics,2018,65(7):5634-43.
[11] ALI U M,ZAFAR A,NENGROO H S,et al. Towards a smarter battery management system for electric vehicle applications:A critical review of lithium-ion battery state of charge estimation[J]. Energies,2019,12(3):446.
[12] WANG L,LU D,LIU Q,et al. State of charge estimation for LiFePO₄ battery via dual extended kalman filter and charging voltage curve[J]. Electrochimica Acta,2019,296:1009-1017.
[13] HOW D N T,HANNAN M A,LIPU M S H,et al. state of charge estimation for lithium-ion batteries using model-based and data-driven methods:A review[J]. IEEE Access,2019,7:136116-136136.
[14] SHRIVASTAVA P,SOON T K,IDRIS M Y I B,et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries[J]. Renewable and Sustainable Energy Reviews,2019,113:109233.
[15] ZHANG L,HU X,WANG Z,et al. A review of supercapacitor modeling,estimation,and applications:A control/management perspective[J]. Renewable and Sustainable Energy Reviews,2018,81:1868-1878.
[16] LI X,WANG Z,YAN J. Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression[J]. Journal of Power Sources,2019,421:56-67.
[17] LIPU M S H,HANNAN M A,HUSSAIN A,et al. A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles:Challenges and recommendations[J]. Journal of Cleaner Production,2018,205:115-133.
[18] LI X,WANG Z,ZHANG L,et al. State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis[J]. Journal of Power Sources,2019,410-411:106-114.
[19] WANG J,LIU P,HICKS-GARNER J,et al. Cycle-life model for graphite-LiFePO4 cells[J]. Journal of Power Sources,2011,196(8):3942-3948.
[20] PAN H,LÜ Z,WANG H,et al. Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine[J]. Energy,2018,160:466-477.
[21] YANG D,ZHANG X,PAN R,et al. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve[J]. Journal of Power Sources,2018,384:387-395.
[22] FARMANN A,SAUER D U. A comprehensive review of on-board state-of-available-power prediction techniques for lithium-ion batteries in electric vehicles[J]. Journal of Power Sources,2016,329:123-137.
[23] CABRERA-CASTILLO E,NIEDERMEIER F,JOSSEN A. Calculation of the state of safety (SOS) for lithium ion batteries[J]. Journal of Power Sources,2016,324:509-520.
[24] FENG X,OUYANG M,LIU X,et al. Thermal runaway mechanism of lithium ion battery for electric vehicles:A review[J]. Energy Storage Materials,2018,10:246-267.
[25] LING Z,ZHANG Z,SHI G,et al. Review on thermal management systems using phase change materials for electronic components,Li-ion batteries and photovoltaic modules[J]. Renewable and Sustainable Energy Reviews,2014,31:427-438.
[26] HUANG P,VERMA A,ROBLES D J,et al. Probing the cooling effectiveness of phase change materials on lithium-ion battery thermal response under overcharge condition[J]. Applied Thermal Engineering,2018,132:521-530.
[27] SCHWEITZER B,WILKE S,KHATEEB S,et al. Experimental validation of a 0-D numerical model for phase change thermal management systems in lithium-ion batteries[J]. Journal of Power Sources,2015,287:211-219.
[28] ZHAO R,GU J,LIU J. An experimental study of heat pipe thermal management system with wet cooling method for lithium ion batteries[J]. Journal of Power Sources,2015,273:1089-1097.
[29] KHATEEB S A,AMIRUDDIN S,FARID M,et al. Thermal management of Li-ion battery with phase change material for electric scooters:experimental validation[J]. Journal of Power Sources,2005,142(1-2):345-353.
[30] ZHAO R,GU J,LIU J. Optimization of a phase change material based internal cooling system for cylindrical Li-ion battery pack and a hybrid cooling design[J]. Energy,2017,135:811-822.
[31] TRAN T H,HARMAND S,DESMET B,et al. Experimental investigation on the feasibility of heat pipe cooling for HEV/EV lithium-ion battery[J]. Applied Thermal Engineering,2014,63(2):551-558.
[32] NAGHAVI M,ONG K,BADRUDDIN I,et al. Theoretical model of an evacuated tube heat pipe solar collector integrated with phase change material[J]. Energy,2015,91:911-924.
[33] ZHAO J,RAO Z,LIU C,et al. Experimental investigation on thermal performance of phase change material coupled with closed-loop oscillating heat pipe (PCM/CLOHP) used in thermal management[J]. Applied Thermal Engineering,2016,93:90-100.
[34] DEY S,BIRON Z A,TATIPAMULA S,et al. Model-based real-time thermal fault diagnosis of lithium-ion batteries[J]. Control Engineering Practice,2016,56:37-48.
[35] SUN J,WEI G,PEI L,et al. Online internal temperature estimation for lithium-ion batteries based on Kalman filter[J]. Energies,2015,8(5):4400-4415.
[36] DAI H,ZHU L,ZHU J,et al. Adaptive Kalman filtering based internal temperature estimation with an equivalent electrical network thermal model for hard-cased batteries[J]. Journal of Power Sources,2015,293:351-365.
[37] International Organization for Standardization,ISO 64691:2019 Electrically propelled road vehicles-Safety specifications-Part 1:Rechargeable energy storage system (RESS)[S]. United Kingdom:British Standards Institution,2019.
[38] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. GB/T 18384.1-2015,电动汽车安全要求第1部分:车载可充电储能系统(REESS)[S]. 北京:中国标准出版社,2015.General Administration of Quality Supervision,Inspection and Quarantine of the Peoples Republic of China,Standardization Administration of the Peoples Republic of China. GB/T 18384.1-2015 Electrically propelled road vehicles-Safety specifications-Part 1:On-board rechargeable energy storage system (RESS)[S]. Beijing:Standards Press of China,2015.
[39] LIAN Y,ZENG D,YE S,et al. High-voltage safety improvement design for electric vehicle in rear impact[J]. Automotive Innovation,2018,1(3):211-225.
[40] KUMAR M N S,BALAKRISHNAN K. Functional safety development of battery management system for electric vehicles[C]//proceedings of the 2019 IEEE Transportation Electrification Conference (ITEC-India),17-19 Dec. 2019,Bengaluru,India. Piscataway:IEEE,2019:1-6.
[41] 陈秀凤. 新能源汽车监控平台的设计与实现[D]. 大连:大连理工大学,2018. CHEN Xiufeng. Design and implementation of new energy vehicle monitoring system[D]. Dalian:Dalian University of Technology,2018.
[42] 苗圩. 新能源汽车生产企业及产品准入管理规定[J]. 中华人民共和国国务院公报,2017,22:38-42.MIAO Wei. Regulations on new energy vehicle manufacturing enterprises and product access management[J]. Bulletin of the State Council of the People's Republic of China,2017,22:38-42.
[43] 唐良,李逸瀚,石春,等. 电动汽车信息安全网关的设计与实现[J]. 计算机应用与软件,2017,34(3):277-83. TANG Liang,LI Yihan,SHI Chun,et al. Design and implementation of information security gateway for electric vehicle[J]. Computer Applications and Software,2017,34(3):277-283.
[44] YUNZHENG Z,SHAOFEI Q,HUANG A C,et al. Smart vehicle control unit-an integrated BMS and VCU system[J]. IFAC-PapersOnLine,2018,51(31):676-679.
[45] SALEHEN P,SUAIT M,RAZALI H,et al. Development of battery management systems (BMS) for electric vehicles (evs) in Malaysia[C]//MATEC Web of Conferences,20 Dec. 2016,Selangor,Malaysia. EDP Sciences,2017,90:01001.
[46] 胡舜耕,王志军,张琳,等. 移动互联汽车数据共享[J]. 电信科学,2016,32(12):26-31. HU Shungeng,WANG Zhijun,ZHANG Lin,et al. Data sharing of mobile internet vehicles[J]. Telecommunications Science,2016,32(12):26-31.
[47] 王帅. 用于电动汽车的隐私保护与数据安全研究[D]. 北京:华北电力大学(北京),2019.WANG Shuai. Research of privacy protection and data security for electric vehicles[D]. Beijing:North China Electric Power University (Beijing),2019.
[48] BOGORNY V,RENSO C,DE AQUINO A R,et al. Constant-a conceptual data model for semantic trajectories of moving objects[J]. Transactions in GIS,2014,18(1):66-88.
[49] 中国汽车工程学会. 智能网联汽车信息安全白皮书[R]. 中国智能网联汽车产业创新联盟成立大会2017-10-17,2017.China-SAE. White paper on intelligent network automobile information security[R]. China Industry Innovation Alliance for Intelligent and Connected Vehicles Conference 2017-10-17,2017.
[50] CHECKOWAY S,MCCOY D,KANTOR B,et al. Comprehensive experimental analyses of automotive attack surfaces[C]//USENIX Security Symposium,812 Aug. 2011,San Francisco,CA,USA. Berkeley:USENIX Association,2011,4:447-462.
[51] ZENG K C,LIU S,SHU Y,et al. All your {GPS} are belong to us:Towards stealthy manipulation of road navigation systems[C]//Proceedings of the 27th USENIX Conference on Security Symposium,1517 Aug. 2018,Baltimore,MD,USA. Berkeley:USENIX Association,2018:1527-1544.
[52] KHURSHID S,PĂSĂREANU C,VISSER W. Most150-development and production launch from an oems perstective[C]//11th MOST Intercon-Nectivity Conference,23 Mar. 2010,Seoul,Korea Switzerland,Yumpu,2010:553-568.
[53] CAMPBELL M,EGERSTEDT M,HOW J P,et al. Autonomous driving in urban environments:approaches,lessons and challenges[J]. Philosophical Transactions of the Royal Society A:Mathematical,Physical and Engineering Sciences,2010,368(1928):4649-4672.
[54] LIN X,SUN X,HO P H,et al. GSIS:A secure and privacy-preserving protocol for vehicular communications[J]. IEEE Transactions on vehicular technology,2007,56(6):3442-3456.
[55] YUAN C,WANG Q,WANG Y,et al. Inhibition effect of different interstitial materials on thermal runaway propagation in the cylindrical lithium-ion battery module[J]. Applied Thermal Engineering,2019,153:39-50.
[56] ASHTIANI C. Analysis of battery safety and hazards' risk mitigation[J]. ECS Transactions,2008,11(19):1.
[57] LI Z,ZHANG J,WU B,et al. Examining temporal and spatial variations of internal temperature in large-format laminated battery with embedded thermocouples[J]. Journal of Power Sources,2013,241:536-553.
[58] SUN Z,LIU P,WANG Z. Real-time fault diagnosis method of battery system based on Shannon entropy[J]. Energy Procedia,2017,105:2354-2359.
[59] YAO L,WANG Z,MA J. Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles[J]. Journal of Power Sources,2015,293:548-561.
[60] HONG J,WANG Z,YAO Y. Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks[J]. Applied Energy,2019,251:113381.
[61] WU C,ZHU C,GE Y. A new fault diagnosis and prognosis technology for high-power lithium-ion battery[J]. IEEE Transactions on Plasma Science,2017,45(7):1533-1538.
[62] 李晓辉,张向文,周永健,等. 模糊神经网络的动力电池故障诊断系统[J]. 电源技术,2019,43(8):1391-1394. LI Xiaohui,ZHANG Xiangwen,ZHOU Yongjian,et al. Power battery fault diagnosis system based on fuzzy neural network[J]. Chinese Journal of Power Sources,2019,43(8):1391-1394.
[63] XIA B,SHANG Y,NGUYEN T,et al. A correlation based fault detection method for short circuits in battery packs[J]. Journal of power Sources,2017,337:1-10.
[64] RICHARDSON R R,OSBORNE M A,HOWEY D A. Battery health prediction under generalized conditions using a Gaussian process transition model[J]. Journal of Energy Storage,2019,23:320-328.
[65] KIM T,MAKWANA D,ADHIKAREE A,et al. Cloud-based battery condition monitoring and fault diagnosis platform for large-scale lithium-ion battery energy storage systems[J]. Energies,2018,11(1):125.
[66] FENG X,WENG C,OUYANG M,et al. Online internal short circuit detection for a large format lithium ion battery[J]. Applied Energy,2016,161:168-180.
[67] YANG R,XIONG R,SHEN W,et al. Extreme learning machine-based thermal model for lithium-ion batteries of electric vehicles under external short circuit[J]. Engineering,2021,7(3):395-405.
[68] MERLA Y,WU B,YUFIT V,et al. Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries[J]. Journal of Power Sources,2016,307:308-319.
[69] YANG R,XIONG R,MA S,et al. Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks[J]. Applied Energy,2020,260:114253.
[70] KANG Y,DUAN B,ZHOU Z,et al. A multi-fault diagnostic method based on an interleaved voltage measurement topology for series connected battery packs[J]. Journal of Power Sources,2019,417:132-144.
[71] ZHAO Y,LIU P,WANG Z,et al. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods[J]. Applied Energy,2017,207:354-362.
[72] ZHAO Y,WANG Z,SHEN Z J M,et al. Data-driven framework for large-scale prediction of charging energy in electric vehicles[J]. Applied Energy,282:116175.
[73] HU X,ZHANG K,LIU K,et al. Advanced fault diagnosis for lithium-ion battery systems:A review of fault mechanisms,fault features,and diagnosis procedures[J]. IEEE Industrial Electronics Magazine,2020,14(3):65-91.
[74] SHANG Y,LU G,KANG Y,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.
[75] HONG J,WANG Z,CHEN W,et al. Multi-fault synergistic diagnosis of battery systems based on the modified multi-scale entropy[J]. International Journal of Energy Research,2019,43(14):8350-69.
[76] XIONG R,SUN W,YU Q,et al. Research progress,challenges and prospects of fault diagnosis on battery system of electric vehicles[J]. Applied Energy,2020,279:115855.
[77] LI K,ZHOU P,LU Y,et al. Battery life estimation based on cloud data for electric vehicles[J]. Journal of Power Sources,2020,468:228192.
[78] SONG L,ZHANG K,LIANG T,et al. Intelligent state of health estimation for lithium-ion battery pack based on big data analysis[J]. Journal of Energy Storage,2020,32:101836.
[79] LI X,YUAN C,LI X,et al. State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression[J]. Energy,2020,190:116467.
[80] 佘承其,张照生,刘鹏,等. 大数据分析技术在新能源汽车行业的应用综述——基于新能源汽车运行大数据[J]. 机械工程学报,2019,55(20):3-16. SHE Chengqi， ZHANG Zhaosheng， LIU Peng， et al. Overview of the application of big data analysis technology in new energy vehicle industry： Based on operating big data of new energy vehicle[J]. Journal of Mechanical Engineering， 2019， 55(20)： 3-16.
[81] HAO X, WANG H, LIN Z, et al. Seasonal effects on electric vehicle energy consumption and driving range:A case study on personal, taxi, and ridesharing vehicles[J]. Journal of Cleaner Production, 2020, 249:119403.
[82] SUN B. A multi-objective optimization model for fast electric vehicle charging stations with wind, PV power and energy storage[J]. Journal of Cleaner Production, 2021, 288:125564.