A Study for Electric Vehicles Lithium-Ion Battery State Joint Estimation: Based on 2-RC Fractional Model

<div>In the pursuit of enhancing the reliability of battery health management methods, accurate estimation of state of charge (SOC) and state of health (SOH) remains a critical challenge. This article presents a novel fusion estimation algorithm, combining a dual extended Kalman filter (EKF) with a particle filter (PF), based on a fractional-order 2-RC battery model (FOEKPF–EKF). The 2-RC fractional-order model (FOM) is first implemented to accurately depict the battery’s discharge behavior, outperforming traditional integer-order models (IOM) due to its ability to capture the cell’s intrinsic diffusion and dispersion characteristics. An adaptive genetic algorithm (AGA) is then employed for optimal parameter identification of the FOM, ensuring precise modeling. Following this, the FOEKPF–EKF algorithm is developed, leveraging the strengths of FOM, EKF, and PF to effectively handle uncertain, time-varying noise, thereby improving SOC estimation accuracy. The reliability of the proposed algorithm is validated through both simulations and experiments. The results demonstrate that the mean error, maximum error, and RMSE of the terminal voltage are 4.4 mV, 37 mV, and 7.1 mV, respectively—significantly lower than those of the IOM. Additionally, the AGA shows higher accuracy compared to the recursive least squares (RLS) method. The FOEKPF–EKF algorithm also achieves more reliable SOC estimation compared to FOEKPF, IOEKPF, and FOPF, with a mean error of 0.48%, a maximum error of 1.23%, and an RMSE of 0.55%. Finally, this article outlines potential directions for future research.</div>