奇偶空间法用于电动车锂离子电池传感器故障诊断

doi:10.19562/j.chinasae.qcgc.2019.07.015

Abstract

Abstract: Based on the first-order equivalent circuit model of the battery, this paper couples the heat generation and heat transfer equations of the battery, and establishes an electro-thermal model that reflects the dynamic response and thermal characteristics of the battery. Based on this, the parity space method is used to detect the fault of the battery output sensor and a fault isolation method based on system matrix operation is proposed. With the random current excitation as the input signal, the battery output sensor fault is analyzed. The results show that the residual generator designed by the parity space approach can accurately detect the battery output sensor fault, and its response to the fault signal is much larger than the oscillation caused by modeling accuracy. The improved residual generator can accurately isolate the fault source based on the detection of the output sensor fault

Key words: lithium-ion battery, electro-thermal model, parity space approach, fault detection and isolation

Pan Fengwen, Ma Bin, Gao Ying, Xu Mingwei ,Gong Dongliang. Parity Space Approach for Fault Diagnosis of Lithium-ion Battery Sensor for Electric Vehicles[J].Automotive Engineering, 2019, 41(7): 831-838.

References

[1] MI C, MASRUR M A, GAO D W. Hybrid electric vehicles: principles and applications with practical perspectives[M]. John Wiley & Sons,2011.
[2] EHSANI M, GAO Y, EMADI A. Modern electric, hybrid electric, and fuel cell vehicles: fundamentals, theory, and design[M]. CRC Press,2009.
[3] Da ROSA A V. Fundamentals of renewable energy processes[M]. Academic Press,2012.
[4] 唐葆君,马也.“十三五”北京市新能源汽车节能减排潜力[J].北京理工大学学报:社会科学版,2016,18(2):13-17.
[5] 李泉.锂离子动力电池管理系统关键技术研究[D].长沙:湖南大学,2017.
[6] SERGIO M, FLORIN M. Electric vehicle battery technologies: from present state to future systems[J]. Renewable and Sustainable Energy Reviews,2015,51:1004-1012.
[7] 赵瑞瑞,余乐,常毅,等.锂离子电池技术研究进展与应用[M].北京:化学工业出版社,2017:244-253.
[8] ANGEL K. Battery management and battery diagnostics[M]. Elsevier Inc.:2015-06-15.
[9] 麻友良,陈全世,齐占宁.电动汽车用电池SOC定义与检测方法[J].清华大学学报:自然科学版,2001(11):95-97,105.
[10] 徐颖,沈英.基于改进卡尔曼滤波的电池SOC估算[J].北京航空航天大学学报,2014,40(6):855-860.
[11] 商云龙,张承慧,崔纳新,等.基于模糊神经网络优化扩展卡尔曼滤波的锂离子电池荷电状态估计[J].控制理论与应用,2016,33(2):212-220.
[12] 张云,张承慧,崔纳新.锂离子电池荷电状态估计:非线性观测器方法[J].控制理论与应用,2012,29(12):1639-1644.
[13] 李哲,卢兰光,欧阳明高.提高安时积分法估算电池SOC精度的方法比较[J].清华大学学报:自然科学版,2010,50(8):1293-1296,1301.
[14] 刘文杰,齐国光.基于模糊理论的电池故障诊断专家系统[J].吉林大学学报:信息科学版,2005,23(6):104-108.
[15] 葛云龙,陈自强,郑昌文.UTSTF锂离子电池时变参数估计与故障诊断[J].浙江大学学报:工学版,2018,52(6):1223-1230.
[16] AURBACH D, ZINIGRAD E, COHEN Y, et al. A short review of failure mechanisms of lithium metal and lithiated graphite anodes in liquid electrolyte solutions[J]. Solid State Ionics, Diffusion & Reactions,2002,148(3-4):405-416.
[17] ALAVI S, SAMADI M, SAIF M F. Diagnostics in lithium-ion batteries: challenging issues and recent achievements[M]. Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives. Heidelberg, Germany: Springer,2013:277-291.
[18] MARCICKI J, ONORI S, RIZZONI G. Nonlinear fault detection and isolation for a lithium-ion battery management system[C]. Proc. ASME Dyn. Syst. Control Conf,2010:607-614.
[19] SINGH A, IZADIAN A, ANWAR S. Fault diagnosis fo li-ion batteries using multiple-model adaptive estimation[C]. Proc. 39^th Annu. Conf. IEEE IECON, Nov.2013:3524-3529.
[20] MUKHOPADHYAY S, ZHANG F. Adaptive detection of terminal voltage collapses for Li-ion batteries[C]. Proc. IEEE 51^st Annu. Conf. Decision Control, Dec.2012:4799-4804.
[21] CHEN W, CHEN W, SAIF M, et al. Simultaneous fault isolation and estimation of lithium-ion batteries via synthesized design of Luenberger and learning observers[J]. IEEE Trans. Control Syst. Technol.,2014,22(1):290-298.
[22] LOMBARDI W, ZARUDNIEV M, LESECQ S, et al. Sensors fault diagnosis for a BMS[C]. Proc. Eur. Control Conf.2014:952-957.
[23] CHOW E Y, WILLSKY A S. Analytical redundancy and the design of robust failure detection systems[J]. IEEE Transactions on Automatic Control,1984,29(7):603-614.
[24] 刘剑慰,姜斌.基于动态奇偶空间法的传感器故障诊断[J].控制工程,2012,19(5):870-872,876.
[25] 吕亮.基于模型的汽车主动悬架故障诊断研究[D].长沙:湖南大学,2018.
[26] HWANG W, HUH K, KIM M, et al. Sensor fault diagnosis for EMB using parity space approach[C]. SAE Paper 2012-01-1794.
[27] HU X, LI S, PENG H. A comparative study of equivalent circuit models for Li-ion batteries[J]. Journal of Power Sources,2012,198:359-367.
[28] 陈全世,林成涛.电动汽车用电池性能模型研究综述[J].汽车技术,2005(3):1-5.
[29] HAN H C, XU H P, YUAN Z Q. Modeling for lithium-ion battery used in electric vehicles[C]. Transportation Electrification Asia-Pacific (ITEC Asia-Pacific),2014 IEEE Conference and Expo,2014.
[30] Limoge Damas Wilks. Reduced-order modeling and adaptive observer design for lithium-ion battery cells[D]. Massachusetts Institute of Technology,2017.

[1]	ZHOU Wei, ZHANG Cheng-Ning, LI Jun-Qiu. [J]. , 2016, 38(12): 1407 -1414 .
[2]	ZONG Yi-Qi, GU Zheng-Qi, LUO Ze-Min, JIANG Cai-Mao, ZHANG Qi-Dong, YANG Zhen-Dong. [J]. , 2016, 38(12): 1427 -1433 .
[3]	ZHANG Ying-Chao, ZHAN Da-Peng, ZHAO Jing, ZHANG Zhe, LI Jie. [J]. , 2016, 38(12): 1434 -1439 .
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Parity Space Approach for Fault Diagnosis of Lithium-ion Battery Sensor for Electric Vehicles

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