A Smart Particle Filter Technology for Battery State Estimation and Life Prediction

Lithium-ion (Li-ion) batteries are commonly employed in a wide range of industrial and household applications. However, the performance of Li-ion batteries degrades over time due to the aging process, which is difficult to measure using general sensors. Although particle filter (PF) technique can be used for modeling the nonlinear degradation features of battery system, it suffers sample degeneracy and impoverishment problems that limit its ability to accurately capture the electrochemical behaviors of a battery system in state estimation. This paper proposes a smart particle filter (SPF) technique to address these problems and improve the performance of PFs. The proposed SPF technique includes two innovative aspects. Firstly, a sample degeneracy detection method is suggested to identify the low-weight particles associated with sample degeneracy. Secondly, a mutation approach is proposed to adaptively explore the posterior probability density function (PDF) and process the low-weight particles to tackle sample degeneracy. Simulation tests have been conducted to verify the effectiveness of the proposed SPF technique. It is also implemented for predicting the remaining useful life (RUL) of Li-ion batteries. The results of the tests indicate that the proposed SPF technique can effectively capture a system's dynamic behavior and track system characteristics.