AI-Enhanced Power Management Strategies for Next-Generation Hybrid Electric Vehicles: Deep Reinforcement Learning and Predictive Analytics

Résumé fourni par la source

This study presents a comprehensive investigation into next-generation Power Management Strategies (PMS) for Hybrid Electric Vehicles (HEVs), focusing on artificial intelligenceenhanced approaches, bidirectional energy flow management, and emerging quantum computing applications. Recent advances in deep reinforcement learning, including Auto-Tune Soft ActorCritic (ATSAC) and offline RL methods, demonstrate significant improvements in real-time adaptability and fuel efficiency. The integration of Vehicle-to-Grid (V2G) technologies opens new paradigms for HEV energy management, enabling vehicles to serve as mobile energy storage units. Furthermore, quantum computing emerges as a promising solution for complex optimization problems beyond classical computing capabilities. By synthesizing recent publications, this study provides a roadmap for intelligent, sustainable, and grid-integrated PMS frameworks for future HEV systems.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.