Energies, Vol. 18, Pages 4597: Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles

Energies, Vol. 18, Pages 4597: Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles

Energies doi: 10.3390/en18174597

Authors:
Dagang Lu
Yu Chen
Yan Sun
Wenxuan Wei
Shilin Ji
Hongshuo Ruan
Fengyan Yi
Chunchun Jia
Donghai Hu
Kunpeng Tang
Song Huang
Jing Wang

Recent breakthroughs in artificial intelligence are accelerating the intelligent transformation of vehicles. Vehicle electronic and electrical architectures are converging toward centralized domain controllers. Deep learning, reinforcement learning, and deep reinforcement learning now form the core technologies of domain control. This review surveys advances in deep reinforcement learning in four vehicle domains: intelligent driving, powertrain, chassis, and cockpit. It identifies the main tasks and active research fronts in each domain. In intelligent driving, deep reinforcement learning handles object detection, object tracking, vehicle localization, trajectory prediction, and decision making. In the powertrain domain, it improves power regulation, energy management, and thermal management. In the chassis domain, it enables precise steering, braking, and suspension control. In the cockpit domain, it supports occupant monitoring, comfort regulation, and human–machine interaction. The review then synthesizes research on cross-domain fusion. It identifies transfer learning as a crucial method to address scarce training data and poor generalization. These limits still hinder large-scale deployment of deep reinforcement learning in intelligent electric vehicle domain control. The review closes with future directions: rigorous safety assurance, real-time implementation, and scalable on-board learning. It offers a roadmap for the continued evolution of deep-reinforcement-learning-based vehicle domain control technology.