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Volume 13
Issue 8
10.3390/machines13080711
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

Fault Diagnosis of In-Wheel Motors Used in Electric Vehicles: State of the Art, Challenges, and Future Directions

by
Yukun Tao
1,2,
Xuan Wang
1,
Liang Zhang
1,
Xiaoyi Bao
1,
Hongtao Xue
1,2,*,
Huiyu Yue
1,2,
Huayuan Feng
1 and
Dongpo Yang
1
1
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2
International Joint Laboratory on Mobility Equipment and Artificial Intelligence for IT Operations, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Machines 2025, 13(8), 711; https://doi.org/10.3390/machines13080711
Submission received: 10 July 2025 / Revised: 3 August 2025 / Accepted: 8 August 2025 / Published: 11 August 2025
(This article belongs to the Special Issue New Journeys in Vehicle System Dynamics and Control)
Versions Notes

Abstract

In-wheel motors (IWMs) have become a promising solution for electric vehicles due to their compact design, high integration, and flexible torque control. However, their exposure to harsh operating conditions increases the risk of mechanical, electrical, and magnetic faults, making reliable fault diagnosis essential for ensuring driving safety and system reliability. Although considerable progress has been made in fault diagnosis techniques related to IWMs, a systematic review in this area is still lacking. To address this gap, this paper provides a comprehensive review of fault diagnosis techniques for IWMs. First, typical faults in IWMs are analyzed with a focus on their unique structural and failure characteristics. Then, the applications and recent research progress of three major categories of fault diagnosis approaches—model-based, signal-based, and knowledge-based methods—in the context of IWMs are critically reviewed. Finally, key challenges and pain points in IWM diagnosis are discussed, along with promising future research directions.
Keywords: in-wheel motor; electric vehicles; machine learning; deep learning; fault diagnosis; model-based diagnosis; signal processing; electromechanical equipment in-wheel motor; electric vehicles; machine learning; deep learning; fault diagnosis; model-based diagnosis; signal processing; electromechanical equipment

Share and Cite

MDPI and ACS Style

Tao, Y.; Wang, X.; Zhang, L.; Bao, X.; Xue, H.; Yue, H.; Feng, H.; Yang, D. Fault Diagnosis of In-Wheel Motors Used in Electric Vehicles: State of the Art, Challenges, and Future Directions. Machines 2025, 13, 711. https://doi.org/10.3390/machines13080711

AMA Style

Tao Y, Wang X, Zhang L, Bao X, Xue H, Yue H, Feng H, Yang D. Fault Diagnosis of In-Wheel Motors Used in Electric Vehicles: State of the Art, Challenges, and Future Directions. Machines. 2025; 13(8):711. https://doi.org/10.3390/machines13080711

Chicago/Turabian Style

Tao, Yukun, Xuan Wang, Liang Zhang, Xiaoyi Bao, Hongtao Xue, Huiyu Yue, Huayuan Feng, and Dongpo Yang. 2025. "Fault Diagnosis of In-Wheel Motors Used in Electric Vehicles: State of the Art, Challenges, and Future Directions" Machines 13, no. 8: 711. https://doi.org/10.3390/machines13080711

APA Style

Tao, Y., Wang, X., Zhang, L., Bao, X., Xue, H., Yue, H., Feng, H., & Yang, D. (2025). Fault Diagnosis of In-Wheel Motors Used in Electric Vehicles: State of the Art, Challenges, and Future Directions. Machines, 13(8), 711. https://doi.org/10.3390/machines13080711

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