Battery State of Energy and Cycle Life Estimation

This chapter introduces the related research contents of battery state of energy (SOE) estimation and remaining useful life (RUL) prediction. In the introduction of SOE estimation, three model-based SOE estimation methods are mainly introduced, namely unscented Kalman filtering, adaptive unscented Kalman filtering, and dual extended Kalman filter. The estimation results can be compared and analyzed in detail. In the introduction of RUL, the definition, function, and main influencing factors of RUL are introduced in detail. Then, the experimental RUL prediction method and data-driven RUL prediction method are introduced in detail. The experimental RUL prediction methods include capacity method, adaptive filtering, and internal resistance method. The data-driven method mainly introduces machine learning, support vector region, and artistic neural networks. Finally, the proposed method is verified under different experimental conditions, and the experimental results are analyzed in detail.