RL-DTNet: Reinforcement Learning Driven Deep Temporal Network for Accurate State-of-Charge Estimation in Lithium-Ion Batteries

Abstract

Accurate state-of-charge (SoC) estimation is essential for ensuring the reliability and operation of lithium-ion batteries in electric vehicles. Traditional data-driven models failed to generalize across varying load profiles. Also, they do not handle nonlinear electrochemical behavior and long-term degradation effects of batteries. Recently, reinforcement learning–based SoC correction techniques have shown promising results in SoC predictions. But these models are based on synthetic or simplified feedback, which is unstable for real-world conditions. The goal of this work is to develop a novel approach for improving SoC prediction in real-time battery management systems. To achieve this, in this work, a reinforcement learning driven deep temporal network (RL-DTNet) is proposed for SoC prediction. The RL-DTNet consists of four major stages: (1) an adaptive feature extraction layer to construct an enriched multidomain representation; (2) a temporal attention gated recurrent unit (GRU) module to focus on the most informative timesteps under varying load disturbances; (3) a reinforcement learning self-correction module using a deep Q-network to learn how to minimize prediction errors by adjusting outputs based on real-time error feedback; and (4) a long-term degradation modeling layer to adjust SoC estimates for aging effects using a cycle-aware degradation factor. The novel contribution of this work is the integration of reinforcement learning for self-correction, temporal attention to handle dynamic dependencies, and a degradation-aware model for long-term prediction accuracy. Experimental results on real-time datasets show that RL-DTNet achieves better results of the root mean squared error, mean absolute error, and coefficient of determination ( R 2 ) of 0.6377, 0.5056, and R 2 , respectively.