A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter

Baohua Mo, Jingsong Yu, D. Tang, Hao Liu
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引用次数: 42

Abstract

The gradual decreasing capacity of lithium-ion batteries can serve as a health indicator to represent the degradation of lithium-ion battery, and through prediction of battery capacity, the remaining useful life (RUL) of battery can be estimated. Quite a few effective methods have been developed for predicting the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries, and particle filtering (PF) is one of them. In this paper, a novel PF-based method for RUL estimation of lithium-ion batteries is developed combining Kalman filter and particle swarm optimization (PSO). First, the standard PF is combined with Kalman filter to increase the accuracy of estimation, and then a particle swarm optimization algorithm is integrated to slow down the particle degradation due to particle resampling. The battery dataset provided by NASA is used to verify the proposed approach. RUL prediction results compared with standard PF and particle swarm optimization-based PF demonstrates the higher accuracy of our proposed method.
基于卡尔曼滤波和改进粒子滤波的锂离子电池剩余使用寿命预测方法
锂离子电池容量的逐渐减小可以作为表征锂离子电池退化的健康指标,通过对电池容量的预测,可以估算出电池的剩余使用寿命。锂离子电池的荷电状态(SOC)和健康状态(SOH)预测已经发展出了许多有效的方法,粒子滤波(PF)就是其中之一。本文将卡尔曼滤波与粒子群优化相结合,提出了一种基于pf的锂离子电池RUL估计方法。首先,将标准滤波器与卡尔曼滤波相结合,提高估计精度,然后结合粒子群优化算法,减缓粒子重采样导致的粒子退化。NASA提供的电池数据集用于验证所提出的方法。与标准PF和基于粒子群优化的PF的RUL预测结果比较,表明本文方法具有较高的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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