A joint real-time SOC estimation method for energy storage batteries

Accurately estimating the state of charge （SOC） of energy storage batteries is of paramount importance for achieving balanced charging and discharging， and mitigating capacity degradation caused by overcharging and overdischarging. In view of the complex chemical states and nonlinear time-varying characteristics of SOC in energy storage batteries， this paper proposes a joint SOC estimation method for lithium-ion batteries based on the variable forgetting factor recursive least squares （VFFRLS） and unscented Kalman filter （UKF） algorithms. The VFFRLS algorithm is employed for online identification of battery model parameters such as resistance and capacitance， and based on the identification results， the UKF algorithm is utilized for real-time SOC estimation. Experimental results demonstrate that the proposed joint method exhibits high accuracy and stability.