AI-ENABLED DESIGN AND OPTIMIZATION OF AEROSPACE ELECTRIC MOTORS: A TASK-ORIENTED REVIEW

Authors

  • SaiBo Wang School of Automation, Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China.
  • HaoZheng Yu School of Automation, Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China.
  • JinXiao Wang School of Automation, Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China.
  • SiYu Wang School of Automation, Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China.
  • Chao Gong School of Automation, Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China.
  • Ke Tang (Corresponding Author) Robotic Division, Xi'an Beide New Data Technology Co., Ltd., Xi’an 710077, Shaanxi, China.

Keywords:

Artificial intelligence, Task-oriented review, Motor design, Aerospace electric motors, Extreme constraints

Abstract

Artificial intelligence (AI) becomes an effective tool in electric motor design. It can speed up design updates, improve optimization in complex conditions, and support physical modeling. However, most existing review papers discuss AI in motor design in a general way. These studies usually do not focus on the special needs of aerospace systems. This paper presents a task-based review of AI methods for aerospace electric motor design under extreme constraints which mainly covers data-efficient learning, surrogate models, multi-objective optimization, constraint-aware optimization, and physics-informed methods. These methods help designers deal with difficult trade-offs among power density, thermal behavior, mechanical strength, and fault tolerance, even when training data are limited. This paper also discusses the main problems in current studies. For example, many methods still lack validation under real flight conditions. In addition, this paper points out several future directions, such as the use of digital twins and AI workflows that can support certification. This review can provide a clear reference for researchers and engineers who apply AI to next-generation aerospace electric propulsion and actuation systems.

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Published

2026-05-11

Issue

Vol. 8 No. 3 (2026)

Section

Research Article

DOI:

https://doi.org/10.61784/jcsee3135

How to Cite

SaiBo Wang, HaoZheng Yu, JinXiao Wang, SiYu Wang, Chao Gong, Ke Tang. Ai-Enabled Design And Optimization Of Aerospace Electric Motors: A Task-Oriented Review. Journal of Computer Science and Electrical Engineering. 2026, 8(3): 34-54. DOI: https://doi.org/10.61784/jcsee3135.