电子科技大学学报2024，Vol.53Issue(5)：749-761,13.DOI:10.12178/1001-0548.2024171
锂电池状态跨域估计算法综述
Cross-Domain State Estimation of Lithium-Ion Batteries:A Review
摘要
Abstract
Accurate state estimation and prediction of lithium-ion battery are crucial for ensuring operational performance and safety.Data-driven state estimation algorithms are prone to the distribution shift between training data and testing data,limiting their generalization capabilities.Transfer-learning-based cross-domain state estimation algorithms are proposed to address these issues.This paper discusses around three common application scenarios:state of charge estimation,state of health estimation,and remaining useful life estimation.While comparing the differences between methods across various scenarios,the review also reveals their commonalities.From a technical perspective,this paper categorizes commonly used transfer methods into three types:finetuning-based transfer,metric-based transfer,and adversarial training-based transfer.Based on these technical approaches,this paper provides a comprehensive and clear summary of recent cross-domain lithium-ion battery state estimation methods.关键词
锂电池状态估计/荷电状态估计/健康状态估计/剩余寿命估计/迁移学习Key words
lithium-ion battery state estimation/state of charge estimation/state of health estimation/remaining useful life estimation/transfer learning分类
信息技术与安全科学引用本文复制引用
李鑫尧,陈洪波,沈力源,冯雪松,李晶晶..锂电池状态跨域估计算法综述[J].电子科技大学学报,2024,53(5):749-761,13.基金项目
国家自然科学基金(62176042) （62176042）
四川省自然科学基金(2023NSFSC0483) （2023NSFSC0483）
