Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model

The precision of equivalent circuit model (ECM)-based state charge (SoC) estimation methods is vulnerable to the variation battery parameters, due several internal and external factors. In this regard, study proposes a fuzzy logic method for approximate ECM parameters at different temperatures SoC levels. inference system designed handle non-linear deviation from their reference values. On basis, extended Kalman filter smooth variable structure are used estimate SoC. two algorithms with (FP), namely FP-EKF FP-SVSF, tested on 20 Ah Nickel Manganese Cobalt cell maximum voltage 4.2 V. results show that root mean square error (RMSE) estimated kept within 1.51% 0.68% FP-SVSF. Moreover, reduction absolute may reach 0.34% FP-EKF, 0.82% compared same without proposed FP method. executable codes implemented low-cost controller, average computational time obtained as 215 μs, which confirms real-time practicality