Combined Online Estimation of State-of-Charge and State-of-Health of Lithium-Ion Power Battery Based on Dual Extended Kalman Filters

Accurate online estimation of the state of charge (SOC) and state of health (SOH) of lithium-ion batteries during charging and discharging processes remains a formidable challenge in Battery Management Systems (BMS). In response to this issue, an effective online estimation method is proposed in this work. Initially, a second-order RC equivalent circuit model is established. Subsequently, to fully adapt to the time-varying characteristics of the model's internal parameters, an improved recursive least square method is integrated to realize the online identification of the model parameters. Ultimately, according to the model parameters identified online, the state equation and the observation equation under the Dual Extended Kalman Filter (DEKF) are established, and the battery capacity is substituted into the DEKF algorithm as a parameter vector to realize the online estimation of the SOC and SOH. By simulating the Federal Urban Driving Status (FUDS), the estimated error of SOC and SOH are, respectively, only 1.14% and 1.55%.