作者：陈万, 蔡艳平, 苏延召, 姜柯, 黄华

作者单位：火箭军工程大学，陕西 西安 710025

关键词：锂离子电池;剩余寿命预测;粒子滤波;无迹卡尔曼滤波;随机扰动重采样

摘要：

针对锂离子电池剩余寿命预测精度不高的问题，提出一种基于随机扰动无迹粒子滤波的锂离子电池剩余寿命预测方法。首先采用无迹卡尔曼滤波算法改进粒子滤波的重要性采样过程，随机扰动重采样算法改进粒子滤波的重采样过程，提出随机扰动无迹粒子滤波算法；然后采用双指数经验模型拟合的方法得到模型参数的初始值；最后采用随机扰动无迹粒子滤波算法对模型参数进行更新，实现锂离子电池的剩余寿命预测并给出了预测结果的概率分布。与基于粒子滤波的方法相比，实验结果表明，该方法的预测结果的绝对误差最多可减小17个周期，绝对误差的变化范围减小9个周期。

Remaining useful life prediction for lithium-ion battery based on improved particle filtering
CHEN Wan, CAI Yanping, SUN Yanzhao, JIANG Ke, HUANG Hua
The Rocket Force Engineering University, Xi’an 710025, China
Abstract: Aiming at the problem that the prediction accuracy of remaining useful life of lithium-ion batteries is not high, a prediction method of remaining useful life of lithium-ion batteries based on random perturbed unscented particle filter is proposed. First, the unscented Kalman filter algorithm is used to improve the importance sampling process of the particle filter, and the random perturbed resampling algorithm is used to improve the resampling process of the particle filter, then the random perturbed unscented particle filter algorithm is proposed. Then the method of double exponential empirical model fitting is used to get the initial value of the model parameters, and finally the random perturbed unscented particle filter algorithm is used to update the model parameters, and the remaining useful life prediction of the lithium-ion battery is realized and the probability distribution of the prediction result is given. Compared with the method based on particle filtering, the experimental results show that the absolute error of the prediction result of the proposed method is reduced by 17 cycles at most, and the variation range of the absolute error is reduced by 9 cycles.
Keywords: lithium-ion battery;remaining useful life prediction;particle filter;unscented Kalman filter;random perturbed resampling
2021, 47(7):148-153  收稿日期: 2020-07-06;收到修改稿日期: 2020-08-23
基金项目:
作者简介: 陈万(1995-),男,陕西西安市人,硕士研究生,专业方向为军用新能源与微网
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