Work

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

Year: 2025

Type: article

Abstract: 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.

Source: IET Power Electronics

DOI: 10.1049/pel2.70101

Authors: Syed M. Ali, Muhammad Bilal, Ayman Alharbi, Rooh ul Amin

Institutions: Shenyang Institute of Engineering, Harbin Engineering University, Umm al-Qura University, University of Engineering and Technology Taxila

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,880