锂电池状态跨域估计算法综述

Cross-Domain State Estimation of Lithium-Ion Batteries: A Review

对锂电池运行状态进行精确估计和预测是保障其运行性能和安全的重要手段。基于数据驱动的锂电池状态估计算法容易受到实际数据分布偏差影响而导致预测模型性能下降,限制了模型的泛化性能,基于迁移学习的锂电池状态跨领域估计算法可以较好地解决此类问题。该文分别从锂电池荷电状态估计、健康状态估计以及剩余寿命估计3类常见应用场景展开讨论,比较不同场景下方法之间的差异,同时揭示它们之间的共性。从技术路线角度出发将常用于状态估计的迁移学习方法归纳为3类:基于微调的迁移、基于度量的迁移和基于对抗训练的迁移。介绍了每一类方法的基本原理、代表性技术和典型应用场景,并基于此3类技术路线对近年基于迁移学习的锂电池状态跨域估计方法进行了全面的归纳介绍。

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:finetuningbased 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.