Fractional-Order Modeling and State of Charge Estimation of Lithium-Ion Batteries for Electric Vehicles

This paper conducts an in-depth study on the fractional-order equivalent circuit modeling and state of charge (SOC) estimation of lithium-ion batteries. Firstly, based on the fractional-order calculus theory, a fractional-order equivalent circuit model (FO-ECM) of lithium-ion batteries is constructed, and the genetic algorithm is employed to precisely identify the model parameters. By comparing and analyzing the simulation results of the model with the experimental data, it is verified that the proposed fractional-order model exhibits high accuracy in describing the dynamic characteristics of the battery. On this basis, a SOC estimation algorithm that integrates the FO-ECM and the adaptive unscented Kalman filter (AUKF) is proposed. The experimental results show that under different dynamic operating conditions, the mean absolute error (MAE) of the proposed algorithm is 0.232% and the root mean square error (RMSE) is 0.217%. Notably, when there is a relatively large deviation of 20% in the initial SOC, the proposed algorithm can rapidly converge to within an error band of 2% of the referenced value within 10 seconds. It demonstrates that the method has excellent robustness and estimation accuracy under complex operating conditions.