Highly robust co‐estimation of state of charge and state of health using recursive total least squares and unscented Kalman filter for lithium‐ion battery

Abstract

AbstractState of charge (SOC) and state of health (SOH) constitute pivotal factors in the efficient and secure management of lithium‐ion batteries, particularly within the context of electric vehicles. A highly‐robust co‐estimation method is proposed in this paper to accurately assess the SOC and SOH under strong electromagnetic interference environment. First, the 1‐RC equivalent circuit model is adopted and the model parameters are identified in a real‐time manner using the recursive total least‐square method to improve the accuracy and adaptivity of the battery model. Subsequently, the SOH estimation is reframed as capacity estimation and an unscented Kalman filter is designed to co‐estimate the SOC and capacity based on the battery model. The results suggest that the proposed method has strong robustness against the measurement noises on current and voltage. The average estimation errors of SOC and capacity are 1.57% and 0.11 Ahr, respectively.