Control
Mohammad Moodi; Mohammad Reza Ramezani-al
Abstract
Accurate state-of-charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries in electric vehicles and energy storage systems. This paper proposes a fusion-based SOC estimation method that integrates two extended Kalman filters (EKFs), each paired with a distinct ...
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Accurate state-of-charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries in electric vehicles and energy storage systems. This paper proposes a fusion-based SOC estimation method that integrates two extended Kalman filters (EKFs), each paired with a distinct open-circuit voltage (OCV)–SOC model. The fusion strategy, grounded in Bayesian probability and residual error analysis, dynamically assigns weights to each model’s output, ensuring that the most appropriate model contributes predominantly to the final SOC estimate at any given moment. The proposed framework utilized a second-order equivalent circuit model (ECM) and estimates parameters online via a variable forgetting factor recursive least squares (VFFRLS) algorithm. Simulation results under LA92 and UDDS driving cycles demonstrate that the method achieves superior accuracy and robustness, reducing the maximum estimation error by up to 26% and RMSE by over 10% compared to conventional EKF approaches. These findings highlight the method’s effectiveness and adaptability for real-time battery management applications.
