关键词: 锂离子电池, 剩余使用寿命, 粒子滤波, Monte Carlo Markov 链, 粒子退化, 无迹粒子滤波

Abstract: In the process of predicting, the remaining useful life of Lithium-ion batteries is based on particle filter algorithm. The fundamental particle filter algorithm has the problem of particle degeneration and it is difficult to ensure the accuracy of the remaining useful life prediction, so an improved unscented particle filter algorithm based on MCMC (Monte Carlo Markov Chain) is proposed. This algorithm overcomes the problem of particle degeneration by selecting the appropriate importance density function and resampling strategy, and improves the accuracy of the remaining useful life prediction. The simulation experiment shows that the improved particle filter algorithm can track the decline trend of battery capacity better and achieve higher precision than the fundamental particle filter algorithm,which can provide a new idea for predicting the remaining useful life of Lithium-ion batteries.

Key words: lithium-ion batteries, the remaining useful prediction, particles degeneracy, Monte Carlo Markov Chain (MCMC), particle filter, unscented particle filter