University of Sistan and Baluchestan International Journal of Industrial Electronics Control and Optimization 2645-3517 Articles in Press 2025 09 28 A State-of-Charge fusion estimation of Lithium-Ion batteries based on the mathematical models of the open circuit voltage curve 9422 10.22111/ieco.2025.52197.1698 EN Mohammad Moodi Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Quchan University of Technology, Quchan, Iran. Mohammad Reza Ramezani-al Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Quchan University of Technology, Quchan, Iran Journal Article 2025 06 23 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. https://ieco.usb.ac.ir/article_9422_e34ce97ffbf906a33c0dbee29ec77836.pdf