Remaining Useful Life Prediction of Lithium-Ion Batteries Based on IMM-PFF

WANG Shuai; LI Yi-ting; CHEN Li-fei; SU Xiao-hong; ZHOU Shou-bin

doi:10.12263/DZXB.20241130

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Remaining Useful Life Prediction of Lithium-Ion Batteries Based on IMM-PFF

PAPERS | 更新时间：2025-08-18

- Remaining Useful Life Prediction of Lithium-Ion Batteries Based on IMM-PFF
- ACTA ELECTRONICA SINICA Vol. 53, Issue 5, Pages: 1520-1532(2025)
- 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：
- 基金信息：
  
  Natural Science Foundation Project of Fujian(2023J01297);National Natural Science Foundation of China(U1805263)
- DOI：10.12263/DZXB.20241130
  CLC： TP206+.3;
- Received：16 December 2024，
  
  Revised：2025-04-30，
  
  Published：25 May 2025
- 稿件说明：
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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.

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Keywords

references

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