基于机器学习的电池剩余使用寿命预测方法综述

Research progress of prediction methods for remaining useful life of battery based on machine learning

电池剩余使用寿命预测是电池管理系统中的关键环节,对于电池的安全运行至关重要。由于电池退化受到诸多因素的影响,剩余寿命预测仍然面临着多方面挑战。近年来,机器学习算法由于强大的非线性学习能力而受到广泛关注,并且逐渐成为剩余使用寿命预测的可靠主流方法。梳理了各类基于机器学习的剩余使用寿命预测算法,分析其优缺点,并总结和展望了未来的改进方向。

Battery remaining useful life prediction technology is a key link in the battery management system,which is essential for the safe operation of the battery.Prediction of remaining useful life still faces multiple challenges as battery degradation is affected by many factors.In recent years,machine learning algorithms have received extensive attention due to their powerful nonlinear learning capabilities and have gradually become mainstream methods for reliable remaining life prediction.Various machine learning-based remaining useful life prediction algorithms were classifies and sorts out,their advantages and disadvantages were analyzed,and future improvement directions were summarized and expected.