A Battery Degradation Prediction Framework Considering Differences in Electric Vehicle Operating Characteristics

Accurate and reliable health status prognostics are critical for ensuring battery safety and enabling smart management. Most existing studies assume stable operating conditions, which contrast with the dynamic and uncertain characteristics of in-service electric vehicles (EVs), thus challenging the practical application of developed approaches. To address this challenge, we propose an adaptable battery degradation prediction framework for EVs with different operating characteristics. Initially, we analyze the operational characteristics of EVs across different application scenarios and introduce a cluster-based charging pattern identification approach. Subsequently, we perform targeted feature extraction based on the identified charging patterns and propose a multilevel feature selection strategy to construct a comprehensive and effective feature pool. Furthermore, we develop two neural network (NN)-based models for reconstructing historical capacity trajectories and predicting battery degradation, further integrating transfer learning to enhance model efficiency and accuracy in unknown scenarios. Finally, we validate the proposed battery health prognostic framework across various training and prediction scenarios, demonstrating its high accuracy and reliability. Specifically, the mean absolute percentage error (MAPE) and root mean square error (RMSE) of degradation prediction are found to be within 1.30% and 2.05% for EVs in different operating scenarios, representing a notable improvement to existing methods.