Intelligent SOC Estimation and Optimization Based on EKF and Fuzzy Logic

Accurate state of charge (SOC) estimation is critical for the efficient management of lithium-ion batteries in electric vehicles and energy storage systems. This study proposes an improved SOC estimation framework by integrating the Extended Kalman Filter (EKF) with fuzzy logic control, optimized using Particle Swarm Optimization (PSO). The proposed method enhances estimation accuracy by dynamically adjusting the Kalman gain and optimizing model parameters. Simulation results demonstrate that the approach significantly reduces estimation errors and improves robustness under varying operating conditions. Compared to conventional methods, the proposed framework exhibits better adaptability to battery nonlinearities and environmental fluctuations. Despite its computational complexity, the method shows strong potential for practical applications in battery management systems. Future work will focus on optimizing real-time implementation and exploring machine learning-based enhancements to further improve performance.