Study of SOH Estimation of EMU Battery Based on Improved Particle Filter

With lithium-ion batteries are more and more widely used in transportation, the estimation of battery state-of-health (SOH) is of great significance in the safe and reliable operation of battery management system and the reduction of maintenance cost. Based on the analysis of the traditional particle filter algorithm, the genetic factor of genetic algorithm (GA) is introduced into the particle filter and improved by adaptive mutation. In order to predict the SOH of lithium-ion battery, the health index (HI) is extracted from the measurable parameters of lithium-ion battery. The mapping model between HI index and SOH is established and applied to the observation of state space model. In this paper, a battery SOH estimation method based on improved particle filter algorithm is proposed. The experimental results show that the proposed method is superior to the traditional particle filter (PF) algorithm and has good accuracy in estimating the degradation process of lithium-ion batteries.