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Review

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

1
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2
School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
3
Research Centre for Electric Vehicles, Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
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China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
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Chongqing Special Equipment Testing and Research Institute (Chongqing Special Equipment Accident Emergency Investigation and Handling Center), Chongqing 401121, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(17), 4597; https://doi.org/10.3390/en18174597
Submission received: 23 July 2025 / Revised: 21 August 2025 / Accepted: 27 August 2025 / Published: 29 August 2025

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 intelligent electric vehicles; domain controller; deep reinforcement learning; multi-domain fusion; transfer learning

Share and Cite

MDPI and ACS Style

Lu, D.; Chen, Y.; Sun, Y.; Wei, W.; Ji, S.; Ruan, H.; Yi, F.; Jia, C.; Hu, D.; Tang, K.; et al. Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles. Energies 2025, 18, 4597. https://doi.org/10.3390/en18174597

AMA Style

Lu D, Chen Y, Sun Y, Wei W, Ji S, Ruan H, Yi F, Jia C, Hu D, Tang K, et al. Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles. Energies. 2025; 18(17):4597. https://doi.org/10.3390/en18174597

Chicago/Turabian Style

Lu, Dagang, Yu Chen, Yan Sun, Wenxuan Wei, Shilin Ji, Hongshuo Ruan, Fengyan Yi, Chunchun Jia, Donghai Hu, Kunpeng Tang, and et al. 2025. "Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles" Energies 18, no. 17: 4597. https://doi.org/10.3390/en18174597

APA Style

Lu, D., Chen, Y., Sun, Y., Wei, W., Ji, S., Ruan, H., Yi, F., Jia, C., Hu, D., Tang, K., Huang, S., & Wang, J. (2025). Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles. Energies, 18(17), 4597. https://doi.org/10.3390/en18174597

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