Estimation for SOC of Electric Vehical Lithium Battery Based on Artificial Immune Particle Filter

In order to accurately predict the SOC value of the electric vehicle, based on the characteristics of the collected data and combined with the empirical battery model, a battery state space model suitable for pure electric vehicles is established. The parameters of the model were calibrated using the forgetting factor recursive least square method. Based on the superiority of particle filter, this paper combines the particle filter with the battery model, and carries out the battery's prediction simulation experiment through the collected real vehicle data. The results show that the results of particle filter algorithm can control the error within a certain range and have certain applicability. In order to increase the diversity of particles, the paper explores the use of artificial immune particle filter(AIPF) to optimize the particle filter(PF). And through comparison experiments with standard particle filters and a large number of statistical experiments, it is proved that the artificial immune particle filter algorithm is more accurate in estimating the lithium battery of a pure electric vehicle. It has a very good accuracy, and can meet actual needs.