Digital Twin and TD3-Enabled Optimization of xEV Energy Management in Vehicle-to-Grid Networks

Abstract

The rapid expansion of extended electric vehicle (xEV) adoption necessitates optimizing energy storage systems (ESS) management for enhanced performance, longevity, and reliability. However, traditional ESS management approaches struggle with real-time state estimation, energy optimization, and predictive maintenance, leading to inefficiencies in battery utilization and sustainability. This paper addresses these challenges by proposing an advanced ESS framework that integrates digital twin (DT) technology with the twin-delayed deep deterministic policy gradient (TD3) algorithm, a state-of-the-art reinforcement learning method derived from the deep deterministic policy gradient (DDPG). This integration enables precise real-time estimation of critical ESS states, including state of charge (SoC), state of health (SoH), state of energy (SoE), and remaining useful life (RUL), thereby enhancing predictive maintenance and operational efficiency. The proposed framework facilitates proactive battery health monitoring, generates early warnings for potential failures, and enables intelligent battery swapping via DT-driven ESS control. By dynamically adapting ESS control strategies, the TD3 algorithm optimizes energy distribution, reduces energy consumption, and improves overall vehicle performance. With a prediction accuracy of 99.8% for SoC, SoH, and SoE, the proposed approach effectively addresses key challenges in xEV ESS management like cell balancing, dynamic charging rate adjustment, battery swapping decisions, and energy optimization, contributing to a more sustainable and efficient EV ecosystem.

Topics & Concepts