基于改进粒子滤波的锂离子电池RUL 预测

在基于粒子滤波算法的锂离子电池剩余使用寿命预测过程中, 由于基本粒子滤波算法存在粒子退化问题, 难以保证电池寿命预测的精度。为此, 提出一种基于MCMC(Monte Carlo Markov Chain)的无迹粒子滤波改进算法, 从选取适当的重要性密度函数和重采样过程两方面入手, 更全面地克服基本粒子滤波算法中的粒子退化问题, 进而提高锂离子电池剩余使用寿命预测的精度。实验仿真结果表明, 改进后的粒子滤波算法能更好地跟踪电池容量衰退趋势, 预测精度也明显优于基本粒子滤波算法, 为锂离子电池剩余使用寿命的预测提供了新思路。

Improved Particle Filter Algorithm for RUL Prediction of Lithium-Ion Batteries

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.