A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation

Abstract Real-time prediction of remaining useful life (RUL) is an essential feature of a robust battery management system (BMS). In this work, a novel method for real-time RUL estimation of Li ion batteries is proposed that integrates classification and regression attributes of Support Vector (SV) based machine learning technique. Cycling data of Li-ion batteries under different operating conditions are analyzed, and the critical features are extracted from the voltage and temperature profiles. The classification and regression models for RUL are built based on the critical features using Support Vector Machine (SVM). The classification model provides a gross estimation, and the Support Vector Regression (SVR) is used to predict the accurate RUL if the battery is close to the end of life (EOL). By the critical feature extraction and the multistage approach, accurate RUL prediction of multiple batteries is accomplished simultaneously, making the proposed method generic in nature. In addition to accuracy, the multistage approach results in faster computations, and hence a trained model can potentially be used for real-time onboard RUL estimation for electric vehicle battery packs.