An enhanced particle filter technology for battery system state estimation and RUL prediction. (15th March 2022) - Ahwiadi, Mohamed; Wang, Wilson
Highlights: The particle filter can model the nonlinear degradation features of battery's system. The proposed enhanced particle technique can recognize and mitigate sample degeneracy. The evolving fuzzy predictor can tackle the problem of lacking new battery measurements during the prognostic period. The developed evolving fuzzy predictor can characterize the input–output mapping for multiple-steps-ahead prediction. Abstract: The particle filter (PF) technique can model nonlinear degradation features of battery's system, and conduct battery state estimation based on noisy measurements. However, PF has some limitations in system state estimation related to sample degeneracy and impoverishment. In addition, its posterior probability density function cannot be updated during the prognostic period due to the absence of new battery measurements. In this work, an enhanced PF technology is proposed to deal with these problems so as to improve PF modeling accuracy for battery state-of-health monitoring and remaining useful life (RUL) prediction. Specifically, an enhanced particles method is proposed to reduce the impact of sample degeneracy and impoverishment in state estimation. An evolving fuzzy predictor is adopted and fused into the enhanced PF structure to deal with the lack of new battery measurements during the prognostic period. The effectiveness of the proposed enhanced PF technology is validated through simulation tests.