Fault Diagnosis of In-Wheel Motors Used in Electric Vehicles: State of the Art, Challenges, and Future Directions
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.
Authors
- Dongpo Yang (ORCID: https://orcid.org/0000-0001-5125-8624)
- Yu-Han Tao (ORCID: https://orcid.org/0009-0001-1793-1149)
- Xiaoyi Bao (ORCID: https://orcid.org/0000-0002-5525-0694)
- Hongtao Xue (ORCID: https://orcid.org/0000-0003-0912-3413)
- Xuan Wang (ORCID: https://orcid.org/0000-0001-7525-5006)
- Huayuan Feng
- Huiyu Yue (ORCID: https://orcid.org/0000-0002-5716-8387)
- Liang Zhang (ORCID: https://orcid.org/0000-0002-7157-8534)
Institutions
- Jiangsu University (CN)
- Beijing Academy of Artificial Intelligence (CN)
Publication Details
- Journal
- Machines
- Published
- 2025-08-11
- DOI
- https://doi.org/10.3390/machines13080711
- Citations
- 1
- Primary Topic
- Machine Fault Diagnosis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Government of Jiangsu Province
- Natural Science Foundation of Jiangsu Province