A Novel Deep Reinforcement Learning Based Extended Fractal Radial Basis Function Network for State‐of‐Charge Estimation

ABSTRACT This paper presents a novel deep reinforcement learning based extended fractal radial basis function (DRL‐EFRBF) network for accurate state‐of‐charge (SOC) estimation in lithium iron phosphate (LiFePO 4 ) batteries. Unlike conventional methods such as open circuit voltage (OCV) and unscented Kalman filter (UKF), the proposed DRL‐EFRBF framework eliminates the need for equilibrium conditions and predefined battery models. The proposed DRL‐EFRBF approach combines adaptive policy learning with fractal‐based nonlinear modeling to capture dynamic battery behaviors without relying on predefined battery models or requiring equilibrium states. The system was implemented using a Texas Instruments BQ76930EVM development kit and interfaced via an STM32 microcontroller. Experimental evaluations under constant, pulsed, and dynamic load profiles demonstrate that DRL‐EFRBF achieves SOC estimation errors below 0.5%, outperforming OCV and UKF by over 90% and 70%, respectively. The proposed method exhibits rapid convergence, adapts to fluctuating conditions, and integrates effortlessly with IoT systems. These findings underscore its appropriateness for real‐time battery management systems in electric vehicles and intelligent energy applications.