Battery health estimation with degradation pattern recognition and transfer learning

Abstract

Battery health monitoring is significant for the maintenance and safety of electric vehicles. Due to huge differences in operation conditions, batteries present diverse degradation patterns (DPs). For battery state of health (SOH) estimation based on data-driven methods, large errors may occur when the DPs of training batteries and test batteries are different. In this paper, early aging data of battery is used to achieve DP recognition and transfer learning (TL), both of which can effectively improve the SOH estimation accuracy. Four features are extracted from discharge capacity curves of battery. Two of them are verified to be highly correlated with battery lifetime and are used to distinguish the DPs of batteries. Others are proved to be closely related to battery capacity and are employed to achieve SOH estimation. Long short-term memory (LSTM) network is used to establish SOH estimation model, and its performance is compared with other machine learning algorithms. Data of 124 cells from public sets is used for verification, and the LSTM is proved to have the best estimation accuracy. Through the proposed DP recognition and TL methods, the estimation accuracy can be further improved, and the mean values of MAEs and RMSEs are only 0.94% and 1.13%.