Real-time SOC and SOH estimation for EV Li-ion cell using online parameters identification

In this paper, we present a real-time adaptive estimation of the state of charge (SOC) and the voltage of a high-energy-density lithium-ion cell used in electric vehicle (EV). We developed a simple and linear-recursive battery SOC and voltage models that proved their efficiency regarding the comparison between simulation results and real data from power cycling with ECE 15 European driving cycle tests. Based on the on-line estimation of battery model parameters, a recursive least squared algorithm (RLS) with a time-variant forgetting factor is used to describe the battery dynamic behavior which can vary within each experiment. The estimations have shown very good performances, the maximum and the mean relative modeling error do not exceed 1% for both of the estimators.