基于改进粒子滤波算法的锂离子电池状态跟踪与剩余使用寿命预测方法

doi:10.19595/j.cnki.1000-6753.tces.190750

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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.

Key words： Improved particle filter algorithm Markov Chain Monte Carlo method li-ion battery state tracking remaining useful life prediction

Received: 21 June 2019

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TM912

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	Jiao Ziquan
	Fan Xingming
	Zhang Xin
	Luo Yi
	Liu Yangsheng

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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.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190750 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I18/3979

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