基于改进红狐算法的粒子滤波锂电池剩余寿命预测

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关键词：锂离子电池；剩余寿命预测；粒子滤波；红狐优化算法

中图分类号： TM912

文献标识码：A

DOI：10.14106/j.cnki.1001-2028.2025.1304

引用格式：孟冠军， 李国强， 彭裕博. 基于改进红狐算法的粒子滤波锂电池剩余寿命预测 [J]. 电子元件与材料， 2025， 44（2）： 184-191.

Reference format： MENG Guanjun， LI Guoqiang， PENG Yubo. Residual life prediction of lithium battery with particle filter based on improved Red Fox algorithm [J]. Electronic Components and Materials， 2025， 44（2）： 184-191.

Residual life prediction of lithium battery with particle filter based

on improved Red Fox algorithm

MENG Guanjun， LI Guoqiang， PENG Yubo

（School of Mechanical Engineering， Hefei University of Technology， Hefei 230009， China）

Abstract：  To further improve the prediction accuracy of the remaining life of lithium batteries， an online capacity estimation method was proposed based on an active detection and Red Fox particle filter algorithm. Based on the standard Red Fox optimization algorithm， an active search strategy was introduced. Chaotic traversal technology was used to generate detection reference points in the search space and actively detected feasible solutions at these reference points， which enhanced the global search capability， and then the sampled particles were transferred to the high likelihood region to reduce the particle depletion problem. To establish the battery capacity degradation model， the characteristic voltage at a fixed time （t=2000 s） was extracted from the discharge curve as a health feature， and a capacity model was then established based on Pearson correlation analysis. Finally， comparative experiments were designed using the NASA dataset for verification. Compared with the prediction results among the standard particle filter algorithm， the unscented particle filter algorithm and the extended particle filter algorithm， the average error of the proposed method is reduced by more than 35%. The results show that the proposed method exhibits higher accuracy and robustness in the state estimation for lithium-ion battery degradation process.

Keywords： lithium-ion battery; residual life prediction; particle filtering; Red Fox optimization algorithm

与常见的铅酸、镍镉电池以及金属氢化物电池相比，锂离子电池具有体积小、电势高、充放电速度快、循环寿命长以及自放电率低等显著优势，因此已广泛应用于动力电池、消费电子等领域。（剩余9561字）