The state of health(SOH)estimation for sodium-ion batteries is crucial for their safe and efficient appli-cations,which is also a key to large-scale energy storage implementations.However,sodium-ion batteries exhibit usage-induced degradation with unclear mechanisms and are sensitive to operating conditions and environmental factors,posing a challenge to the accurate SOH estimation.In this paper,a data-driven SOH estimation method for sodium-ion batteries is proposed.The charging data is correlated with capacity degradation,and variance filtering,grey relational analysis and recursive feature elimination are integrated for feature selection.In addition,four machine learning methods including multiple linear regression,support vector machine,Gaussian process regression and error back propagation neural net-work are applied to formulate the corresponding estimation methods.Test results reveal that the root mean square errors for the four methods are all less than 1.6％,with Gaussian process regression showing an error rate below 0.8％,indicat-ing a precise SOH estimation for sodium-ion batteries.
关键词
钠离子电池/健康状态/数据驱动/老化特征/机器学习
Key words
Sodium-ion battery/state of health(SOH)/data driven/aging feature/machine learning
维普
万方数据