Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles

Dagang Lu, Yu Chen, Yan Sun, Wenxuan Wei, Shilin Ji, Hongshuo Ruan, Fengyan Yi, Chunchun Jia, Donghai Hu (), Kunpeng Tang, Song Huang and Jing Wang
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Dagang Lu: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Yu Chen: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Yan Sun: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Wenxuan Wei: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Shilin Ji: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Hongshuo Ruan: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Fengyan Yi: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Chunchun Jia: Research Centre for Electric Vehicles, Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Donghai Hu: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Kunpeng Tang: China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
Song Huang: Chongqing Special Equipment Testing and Research Institute (Chongqing Special Equipment Accident Emergency Investigation and Handling Center), Chongqing 401121, China
Jing Wang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2025, vol. 18, issue 17, 1-52

Abstract: 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.

Keywords: intelligent electric vehicles; domain controller; deep reinforcement learning; multi-domain fusion; transfer learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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