Accurate State Estimation of Lithium-Ion Batteries via a Fractional-Order Model: Synergistic Integration of Snake Optimizer and Fractional-Order Unscented Kalman Filter

Abstract

Against the backdrop of worsening traditional energy challenges, the automotive industry is shifting from gaso-line/diesel vehicles to new energy alternatives, with lithium-ion battery electric vehicles taking center stage. Accu-rate state of charge (SOC) estimation is critical for battery management systems, relying on precise battery model-ing. This study aims to develop a high-precision framework for battery modeling and SOC estimation in electric vehicles. To accurately simulate battery behavior across frequency bands, an equivalent circuit model integrated with fractional calculus is used instead of traditional capacitors with integer-order capacitors. For precise parame-ter identification, the Snake Optimization (SO) algorithm is employed based on the model’s characteristics. Com-pared with the integer-order counterparts, the advantage of fractional-order model is verified. SOC estimation uses the Fractional-Order Unscented Kalman Filter (FOUKF), with the SO algorithm optimizing the filter matrix’s initial values to enhance estimation. Experimental results show the optimized strategy significantly improves SOC accu-racy, with maximum error ≤1.5% and convergence error ≤0.5%, confirming the practicality of the proposed frac-tional-order circuit model and SO-FOUKF algorithm.