Application-Oriented State-of-Charge Estimation of Lithium-ion Batteries Based on Appropriate Modeling and EKF

The state of charge (SOC) estimation is essential for battery management systems (BMS), necessitating proper modeling and filtering approaches. This study zeroes in on enhancing the accuracy of SOC estimation of battery thanks to an appropriate modeling technique. Considering comprehensive criteria for model structure identification (AIC, BIC, CIC), a second-order RC equivalent circuit model has been chosen. Whose estimation of parameters is transformed into a nonlinear optimization challenge. The cuckoo search algorithm, acclaimed for multi-objective optimization, is employed with a well-defined fitness function, facilitating the determination of optimized parameters. Subsequently, in view of real-world applications, the extended Kalman filter (EKF) algorithm is applied to estimate the SOC with the above optimized second order model. Experimental data analysis affirms that, with appropriate dimensions and iterations, this algorithm efficiently and accurately solves for modeling and SOC estimation of lithium-ion batteries, as well as bolstering its advantages in BMS applications.