A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration

Accurately estimating the state of health (SOH) a lithium-ion battery is significant for electronic devices. To solve nonlinear degradation problem batteries (LIB) caused by capacity regeneration, this paper proposes new LIB model and improved particle filter algorithm SOH estimation. Firstly, process divided into normal stage regeneration stage. A multi-stage prediction (MPM) based on calendar time proposed. Furthermore, genetic embedded standard to increase diversity particles improve accuracy. Finally, method verified with dataset provided NASA Ames Prognostics Center Excellence. The experimental results show that proposed in can effectively accuracy prediction.