Artificial Intelligence and Digital Twin Technologies for Power Converter Control in Transportation Applications: A Review

Huang, Zhen; Gong, Jiawei; Xiao, Xuechun; Gao, Yuan; Xia, Yonghong; Wheeler, Pat; Ji, Bing

Abstract

The rapid electrification across transportation sectors has promoted extensive adoption of electrical power systems. Power electronic converters play a crucial role as components within these systems, enabling efficient and stable system operation through sophisticated control strategies. However, traditional approaches to power converter control often cannot deliver the rapid response and robust control capability in handling nonlinear systems needed in these applications. With the rapid advancement of computational capabilities and various simulation technologies, advanced information technologies such as Artificial Intelligence (AI) and Digital Twin (DT) can significantly enhance control performance by leveraging powerful algorithms and high-fidelity models. AI and DT have been proven to be efficient and reliable tools in addressing these challenges. This review critically examines the application of AI and DT technologies in power converter control for electrical power systems on transportation platforms, analyzing DT models from the perspective of AI algorithms and offering insights for their deeper integration. Finally, the review identifies ongoing challenges and future trends in this field, providing valuable resources for researchers and practitioners involved in developing power converter control of onboard electrical power systems.

Citation

Huang, Z., Gong, J., Xiao, X., Gao, Y., Xia, Y., Wheeler, P., & Ji, B. (2025). Artificial Intelligence and Digital Twin Technologies for Power Converter Control in Transportation Applications: A Review. IET Power Electronics, 18(1), https://doi.org/10.1049/pel2.70013

• Journal Article Type: Review
• Acceptance Date: Feb 10, 2025
• Online Publication Date: Feb 20, 2025
• Publication Date: Jan 1, 2025
• Deposit Date: Mar 12, 2025
• Publicly Available Date: Mar 12, 2025
• Journal: IET Power Electronics
• Electronic ISSN: 1755-4543
• Publisher: Institution of Engineering and Technology (IET)
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 1
• DOI: https://doi.org/10.1049/pel2.70013
• Keywords: artificial intelligence; DC-AC power convertors; DC-DC power convertors; digital twin; motor drives; power converter control; transportation electrification
• Public URL: https://nottingham-repository.worktribe.com/output/45863185
• Publisher URL: https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/pel2.70013
• Additional Information: Received: 2023-12-08; Accepted: 2025-02-10; Published: 2025-02-20