Abstract A standard particle filter (PF) algorithm has the problems of low estimation accuracy, unstable algorithm and low computational efficiency in predicting the remaining useful life (RUL) of li-ion batteries. This paper presents a state tracking and RUL prediction estimation method based on an improved particle filter algorithm. Using the empirical physical model of battery capacity degradation, Bayesian theory is used to model the state tracking of historical samples and optimize training algorithm to determine physical model parameters and resampling strategy. The latest measurement information optimized by the state tracking training is used to replace the measurement information without considering the observation noise in the sequential importance sampling, which guides the generation of new proposed distributions and updates particle importance weights to improve particle degradation. Meanwhile, Metropolis-Hastings sampling algorithm based on Markov Chain Monte Carlo (MCMC) method enriches the diversity of sampling particles, and improves Sampling Importance Resampling (SIR) to solve the problem of particle depletion. The simulation reveals the influence law of model parameters on the prediction results, such as different tracking sets S and particle numbers M. Then the optimal fusion model of state tracking and RUL prediction based on real-time update proposal distribution is constructed combined with MCMC method and PF algorithm. It is an updated and improved particle filter fusion model based on MCMC. The simulation results show that the improved algorithm has good state tracking fitting, high prediction accuracy and good computational efficiency. The simulations of different types of battery capacity and algorithm models under various combined schemes are designed. It is proved that the improved algorithm has strong stability robustness, generalization adaptability and general availability.
Jiao Ziquan,Fan Xingming,Zhang Xin等. State Tracking and Remaining Useful Life Predictive Method of Li-ion Battery Based on Improved Particle Filter Algorithm[J]. Transactions of China Electrotechnical Society, 2020, 35(18): 3979-3994.
[1] 张振宇, 汪光森, 聂世雄, 等. 脉冲大倍率放电条件下磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2019, 34(8): 1769-1779. Zhang Zhenyu, Wang Guangsen, Nie Shixiong, et al.State of charge estimation of LiFePO4 battery under the condition of high rate pulsed discharge[J]. Transactions of China Electrotechnical Society, 2019, 34(8): 1769-1779. [2] 郑志坤, 赵光金, 金阳, 等. 基于库仑效率的退役锂离子动力电池储能梯次利用筛选[J]. 电工技术学报, 2019, 34(增刊1): 388-395. Zheng Zhikun, Zhao Guangjin, Jin Yang, et al.The reutilization screening of retired electric vehicle Lithium-ion battery based on coulombic efficiency[J]. Transactions of China Electrotechnical Society, 2019, 34(S1): 388-395. [3] 郭永芳, 黄凯, 李志刚. 基于短时搁置端电压压降的快速锂离子电池健康状态预测[J]. 电工技术学报, 2019, 34(19): 3968-3978. Guo Yongfang, Huang Kai, Li Zhigang.Fast state of health prediction of Lithium-ion battery based on terminal voltage drop during rest for short time[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 3968-3978. [4] 黄凯, 郭永芳, 李志刚. 基于信息反馈粒子群的高精度锂离子电池模型参数辨识[J]. 电工技术学报, 2019, 34(增刊1): 378-387. Huang Kai, Guo Yongfang, Li Zhigang.High preci- sion parameter identification of Lithium-ion battery model based on feedback particle swarm optimization algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(S1): 378-387. [5] Darcovich K, MacNeil D, Recoskie S, et al. Coupled numerical approach for automotive battery pack lifetime estimates with thermal management[J]. Journal of Electrochemical Energy Conversion and Storage, 2018, 15(2): 021004-1-021004-12. [6] Ning G, White R E, Popov B N.A generalized cycle life model of rechargeable li-ion batteries[J]. Electro- chimica Acta, 2006, 51(10): 2012-2022. [7] Zhang Qi, White R E.Capacity fade analysis of a lithium ion cell[J]. Journal of Power Sources, 2008, 179(2): 793-798. [8] An D, Choi J H, Kim N H.Prognostics 101: a tutorial for particle filter-based prognostics algorithm using Matlab[J]. Reliability Engineering & System Safety, 2013, 115: 161-169. [9] Saha B, Goebel K, Christophersen J.Comparison of prognostic algorithms for estimating remaining useful life of batteries[J]. Transactions of the Institute of Measurement and Control, 2009, 31(3-4): 293-308. [10] Kotecha J H, Djuric P M.Gaussian particle filtering[J]. IEEE Transactions on Signal Processing, 2003, 51(10): 2592-2601. [11] 谢长君, 费亚龙, 曾春年, 等. 基于无迹粒子滤波的车载锂离子电池状态估计[J]. 电工技术学报, 2018, 33(17): 3958-3964. Xie Changjun, Fei Yalong, Zeng Chunnian, et al.State-of-charge estimation of lithium-ion battery using unscented particle filter in vehicle[J]. Transa- ctions of China Electrotechnical Society, 2018, 33(17): 3958-3964. [12] 毕晓君, 胡菘益. 基于混合引导策略的高精度萤火虫优化粒子滤波算法[J]. 上海交通大学学报: 自然版, 2019, 53(2): 232-238. Bi Xiaojun, Hu Songyi.Firefly algorithm with high precision mixed strategy optimized particle filter[J]. Journal of Shanghai Jiaotong University: Science, 2019, 53(2): 232-238. [13] 何耀, 曹成荣, 刘新天, 等. 基于可变温度模型的锂电池SOC估计方法[J]. 电机与控制学报, 2018, 22(1): 43-52. He Yao, Cao Chengrong, Liu Xintian, et al.SOC estimation method for lithium battery based on variable temperature model[J]. Electric Machines and Control, 2018, 22(1): 43-52. [14] 刘月峰, 赵光权, 彭喜元. 锂离子电池循环寿命的融合预测方法[J]. 仪器仪表学报, 2015, 36(7): 1462-1469. Liu Yuefeng, Zhao Guangquan, Peng Xiyuan.A fusion prediction method of lithium-ion battery cycle life[J]. Chinese Journal of Scientific Instrument, 2015, 36(7): 1462-1469. [15] 杜蜀薇, 彭楚宁, 徐石明, 等. 基于贝叶斯层次模型的电能表检定装置在线核查方法[J]. 电力系统自动化, 2018, 42(18): 177-181. Du Shuwei, Peng Chuning, Xu Shiming, et al.Online verification method of meter calibration equipment based on hierarchical Bayes model[J]. Automation of Electric Power Systems, 2018, 42(18): 177-181. [16] 陈志敏, 吴盘龙, 薄煜明, 等. 基于自控蝙蝠算法智能优化粒子滤波的机动目标跟踪方法[J]. 电子学报, 2018, 46(4): 886-894. Chen Zhimin, Wu Panlong, Bo Yuming, et al.Adaptive control bat algorithm intelligent optimization particle filter for maneuvering target tracking[J]. Acta Electronica Sinica, 2018, 46(4): 886-894. [17] 张琪, 张志利, 李天梅, 等. 一种摄动粒子滤波故障检测方法[J]. 电机与控制学报, 2017, 21(11): 103-109. Zhang Qi, Zhang Zhili, Li Tianmei, et al.A particle filter with perturbation for fault detection[J]. Electric Machines and Control, 2017, 21(11): 103-109. [18] Zhang Min, Jia Haitao, Shen Zhen.Improved resampling procedure based on genetic algorithm in particle filter[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(3): 344-349. [19] 朱晓荣, 王羽凝, 金绘民, 等. 基于马尔科夫链蒙特卡洛方法的光伏电站可靠性评估[J]. 高电压技术, 2017, 43(3): 1034-1042. Zhu Xiaorong, Wang Yuning, Jin Huimin, et al.Reliability evaluation of photovoltaic power plant based on Markov Chain Monte Carlo method[J]. High Voltage Engineering, 2017, 43(3): 1034-1042. [20] 中华人民共和国科学技术部. 2008年度EV用锂离子动力蓄电池性能测试规范[Z]. 4版. 北京: 科技部863现代交通技术领域办公室, 2008. [21] 彭喜元, 彭宇, 刘大同. 数据驱动的故障预测[M].哈尔滨: 哈尔滨工业大学出版社, 2016. [22] Wright R B, Motloch C G, Belt J R, et al.Calendar- and cycle-life studies of advanced technology deve- lopment program generation 1 lithium-ion batteries[J]. Journal of Power Sources, 2002, 110(2): 445-470. [23] He Wei, Williard N, Osterman M, et al.Prognostics of lithium-ion batteries based on dempster-shafer theory and the bayesian Monte Carlo method[J]. Journal of Power Sources, 2011, 196(23): 10314-10321. [24] 张凝, 徐皑冬, 王锴, 等. 基于粒子滤波算法的锂离子电池剩余寿命预测方法研究[J]. 高技术通讯, 2017, 27(8): 699-707. Zhang Ning, Xu Aidong, Wang Kai, et al.Research on prediction of the remaining useful life of lithium- ion batteries based on particle filtering[J]. Chinese High Technology Letters, 2017, 27(8): 699-707.
2020, Vol. 35 
