aspg-3-3452-1791417659 20261008000059 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 18 1 Hari Prasad Bhupathi Research Scholar, Dept. of Electronics Engineering, Kalinga University, Raipur, New Raipur, Chhattisgarh, India and Application Software Supervisor, SWX Department, Stellantis NV, Auburn Hill Srikiran Chinta Research Scholar, Dept. of Electronics Engineering, Kalinga University, Raipur, New Raipur, Chhattisgarh, India and Senior Controls Engineer, Hyzon Motors, 3515 Willoway Dr, Troy, Michigan 48 Vijayalaxmi Biradar Prof., Dept. of EE and Director, IQAC, Kalinga University, Raipur, New Raipur, Chhattisgarh, India Sanjay Kumar Suman Prof., Dept. of ECE, Dean R&D, St. Martin’s Engineering College, Secunderabad, Telangana, India Improving the Extended Kalman Filter's (EKF) State of Charge (SOC) prediction for EV battery packs is the primary goal of this section. Optimised batteries management procedures rely on SOC estimate that is both accurate and reliable. The EKF is a popular tool for estimating nonlinear states, but how well it works relies heavily on which noise coefficient matrices are used (Q and R). Experimental testing and other conventional approaches of calibrating these matrix systems are extremely costly and time-consuming. In order to tackle this, the section delves into the integration of four state-of-the-art metaheuristic optimisation methods: GA, PSO, SFO, and HHO. By minimising the mean square error (MSE) among the real and expected SOC, these techniques optimise the Q and R matrices. When looking at preciseness, converging speed, and resilience, SFO-EKF comes out on top in both static and dynamic comparisons. By greatly improving the reliability of SOC estimations, the numerical results show that SFO-EKF obtains the lowest MSE & RMSE. This study advances electric car batteries by providing a realistic scheme for combining optimisation methods with EKF to offer highly effective and exact SOC estimates. When as opposed to TR-EKF, GA-EKF, PSO-EKF, and HHO-EKF, the SFO-EKF approach shows the best accuracy, with an improvement of over 94%. This is a result of the suggested model's exceptional efficiency in SOC estimates. 2025 90 103 3452 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.180108 https://www.americaspg.com/journal/3/article/3452 https://www.americaspg.com/storage/51735527591.pdf