基于智能数模融合的锂离子电池剩余使用寿命预测  

Prediction of Remaining Useful Life of Lithium-Ion Batteries Based on Intelligent Digital-Analogue Fusion

作　　者：周文璐 郑燕萍[1] 杨丞[2] 晏莉琴[2] Zhou Wenlu;Zheng Yanping;Yang Cheng;Yan Liqin(College of Automobile and Traffic Engineering,Nanjing Forestry University,Nanjing 210037;Shanghai Institute of Space Power Supply,Shanghai 200000)

机构地区：[1]南京林业大学汽车与交通工程学院,南京210037 [2]上海空间电源研究所,上海200000

出　　处：《汽车技术》2025年第2期55-62,共8页Automobile Technology

摘　　要：为了提高电池剩余使用寿命(RUL)的预测准确性,基于融合健康指标和构建的电池容量衰退模型,采用粒子群(PSO)优化极限学习机(ELM),结合随机扰动无迹粒子滤波(RP-UPF)的智能数模融合方法对B0005、B0006、B0018号电池的RUL进行预测。研究结果表明:该方法在电池的整个生命周期保持了较高的预测准确性,同时,显著提升了电池RUL预测的精度。In order to improve the accuracy of predicting the Remaining Useful Life(RUL)of batteries,an intelligent digital-analogue fusion method of Particle Swarm Optimization(PSO)optimized Extreme Learning Machine(ELM)combined with Random Perturbation Untraceable Particle Filtering(RP-UPF)is used to predict the RUL of batteries B0005,B0006 and B0018 based on fusion of the health indexes and the constructed battery capacity decline model.The research results show that the proposed intelligent digital-analogue fusion method not only significantly improves the accuracy of battery RUL prediction,but also maintains high prediction accuracy throughout the life cycle of the battery.

关 键 词：锂离子电池 剩余使用寿命 融合健康指标 智能数模融合方法 

分 类 号：TM912[电气工程—电力电子与电力传动]