Bridging Accuracy and Efficiency: A Hybrid Approach for SOC Estimation

This paper presents an analysis of State of Charge (SOC) estimation using two primary methods—Coulomb counting (CC) and adaptive Kalman filtering (AKF)—and explores a hybrid approach that combines CC with Kalman filter-based error correction. Accurate SOC and State of Health (SOH) estimation are crucial for ensuring the longevity, safety, and performance of batteries, particularly in critical applications such as electric vehicles. Our MATLAB-based model simulates each method independently, illustrating the simplicity yet susceptibility to drift in Coulomb counting, and the high accuracy yet computational intensity of adaptive Kalman filtering. We then analyze the benefits of hybridization, showing how the combined approach leverages CC’s efficiency and AKF’s accuracy. Through a comparison of error-correction capabilities, it is evident that while the hybrid method may not reach the stability and precision of standalone AKF, it effectively balances accuracy and computational requirements, providing a viable solution for real-time battery management systems in resource-constrained settings. This research highlights the potential impact of advanced SOC estimation techniques in enhancing battery monitoring and prolonging battery life.