基于IMM-PFF的锂离子电池剩余寿命预测

王帅; 李义婷; 陈黎飞; 苏小红; 周寿斌

doi:10.12263/DZXB.20241130

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基于IMM-PFF的锂离子电池剩余寿命预测

学术论文 | 更新时间：2025-08-18

- 基于IMM-PFF的锂离子电池剩余寿命预测
- Remaining Useful Life Prediction of Lithium-Ion Batteries Based on IMM-PFF
- 电子学报 2025年53卷第5期页码：1520-1532
- 作者机构：
  
  1.福建师范大学计算机与网络空间安全学院，福建福州 350117
  2.哈尔滨工业大学计算学部，黑龙江哈尔滨 150001
  3.华富储能新技术股份有限公司，江苏扬州 225699
- 作者简介：
  
  [ "王帅女，1985年8月生，山东莱州人.福建师范大学讲师.主要研究方向为故障诊断、寿命预测. E-mail: wangshuaiss@fjnu.edu.cn" ]
  [ "李义婷女，1998年9月生，福建三明人.福建师范大学硕士研究生.主要研究方向为寿命预测、机器学习. E-mail: lyt2018zy@hotmail.com" ]
  [ "陈黎飞男，1972年12月生，福建福州人.福建师范大学教授、博士生导师.主要研究方向为机器学习与数据挖掘. E-mail: clfei@fjnu.edu.cn" ]
  [ "苏小红女，1966年10月生，黑龙江哈尔滨人.哈尔滨工业大学教授.主要研究方向为智能软件工程、寿命预测. E-mail: sxh@hit.edu.cn" ]
  [ "周寿斌男，1974年8月生，江苏高邮人.江苏华富储能新技术股份有限公司技术中心主任.主要研究方向为储能材料及产品的研发、高安全性动力电池及其热管理. E-mail: 595085743@qq.com" ]
- 基金信息：
  
  福建省自然科学基金(2023J01297);国家自然科学基金(U1805263)
- DOI：10.12263/DZXB.20241130
  中图分类号： TP206+.3;
- 收稿：2024-12-16，
  
  修回：2025-04-30，
  
  纸质出版：2025-05-25
- 稿件说明：
移动端阅览
王帅, 李义婷, 陈黎飞, 等. 基于IMM-PFF的锂离子电池剩余寿命预测[J]. 电子学报, 2025, 53(05): 1520-1532.

WANG Shuai, LI Yi-ting, CHEN Li-fei, et al. Remaining Useful Life Prediction of Lithium-Ion Batteries Based on IMM-PFF[J]. Acta Electronica Sinica, 2025, 53(05): 1520-1532.
王帅, 李义婷, 陈黎飞, 等. 基于IMM-PFF的锂离子电池剩余寿命预测[J]. 电子学报, 2025, 53(05): 1520-1532. DOI：10.12263/DZXB.20241130

WANG Shuai, LI Yi-ting, CHEN Li-fei, et al. Remaining Useful Life Prediction of Lithium-Ion Batteries Based on IMM-PFF[J]. Acta Electronica Sinica, 2025, 53(05): 1520-1532. DOI：10.12263/DZXB.20241130

摘要

针对单一容量衰退模型在锂离子电池剩余寿命（Remaining Useful Life，RUL）预测中工况泛化能力不足的问题，本文提出一种基于交互式多模型粒子流滤波（Interactive Multiple Model Particle Flow Filter，IMM-PFF）的预测方法.通过粒子流滤波对指数、多项式和生物模型进行协同状态估计，并基于交互式多模型框架动态融合多模型预测结果，从而自适应匹配电池衰退的多阶段特性.将美国NASA、马里兰大学等不同工况的锂离子电池退化数据集划分为3个时期，对本文的方法进行验证.结果表明，相比单一模型粒子滤波方法，IMM-PFF的容量预测均方根误差和剩余寿命预测误差分别降低24.3%和4.5%，为复杂工况下的锂离子电池寿命预测提供了高精度、强鲁棒性的新思路.

Abstract

To address the limited generalization capability of single capacity degradation models in predicting the remaining useful life (RUL) of lithium-ion batteries under varying operating conditions

this paper proposes a prediction method based on the interactive multiple model particle flow filter (IMM-PFF). The method employs particle flow filter to collaboratively estimate the states of exponential

polynomial

and Verhulst models

and dynamically integrates multi-model predictions within an interactive multiple model framework

thereby adaptively matching the multi-phase characteristics of battery degradation. Experimental validation is conducted using lithium-ion battery degradation datasets (NASA and CALCE) under diverse operating conditions

which are divided into three distinct degradation phases. Results demonstrate that compared to single-model particle filter methods

the IMM-PFF reduces the root mean square error (RMSE) of capacity prediction and the absolute RUL prediction error by 24.3% and 4.5%

respectively. This study provides a novel high-precision and highly robust framework for lithium-ion batteries lifespan prediction in complex operational scenarios.

关键词

Keywords

references

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