Remaining Useful Life Prediction of Lithium-Ion Batteries using Semi-Empirical Model and Bat-Based Particle Filter

Lithium-ion batteries (LIBs) are widely adopted in electric vehicles and electronic equipments due to their high energy density, low self-discharge rate, and long cycle life. However, LIBs suffer from capacity fade with cyclic usage. Prediction of remaining useful life (RUL) is therefore essential for ensuring the reliability and safety of LIBs. This paper proposes a bat-based particle filter (PF) algorithm using a semi-empirical model to predict RUL. The semi-empirical model is adopted as the capacity degradation model. Model parameters are then updated by the bat-based PF algorithm. The prediction results of the proposed prediction approach are compareed with PF methods with different resampling algorithms. Simulation results show that the proposed prediction approach can achieve better performance in RUL prediction.