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International Journal of New Developments in Engineering and Society, 2023, 7(3); doi: 10.25236/IJNDES.2023.070306.

Reliability Study of Electric Vehicle Drive Motor Control System

Author(s)

Xiangjie Tian1, Xiaopeng Li2, Yongyuan Chi3

Corresponding Author:
Xiangjie Tian
Affiliation(s)

1School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, China

2School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, China

3School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, China

Abstract

This paper focuses on the study of the reliability of the control system for electric vehicle drive motors. The methods employed include fault tree analysis, failure mode and effects analysis, typical fault mode analysis, and Bayesian uncertainty inference. Firstly, typical fault mode analysis was used to thoroughly understand the causes and characteristics of the faults and to predict and evaluate the fault modes. Secondly, a fault tree for the control system of the drive motor was constructed and FMEA analysis was carried out, identifying the potential impact and failure causes of various fault modes. Finally, Bayesian networks were used to infer the potential fault probability of the controller, providing scientific basis and support for the fault diagnosis system, and reducing maintenance and repair costs. The methods used in this paper can provide reference for the reliability research of electric vehicles.

Keywords

electric vehicle, drive control system, reliability, Bayesian network, FMEA, fault tree

Cite This Paper

Xiangjie Tian, Xiaopeng Li, Yongyuan Chi. Reliability Study of Electric Vehicle Drive Motor Control System. International Journal of New Developments in Engineering and Society (2023) Vol.7, Issue 3: 36-41. https://doi.org/10.25236/IJNDES.2023.070306.

References

[1] Ducote B. Lobitz D. Virost and M. A. Martin, "Failsafe Motor Control Design to Prevent Runaway Motors," in IEEE Transactions on Industry Applications, vol. 58, no. 4, pp. 4271-4278, July-Aug. 2022.

[2] W. Zhao, Y. Lv, X. Guo and J. Huo, "An Investigation on Early Fault Diagnosis Based on Naive Bayes Model," 2022 7th International Conference on Control and Robotics Engineering (ICCRE), Beijing, China, 2022, pp. 32-36.

[3] Y. Yang, H. Mu, X. Yi, H. Yan and G. Chen, "Reliability Analysis and Fault Diagnosis of Command and Control Network Based on Bayes-GO Method," 2019 Prognostics and System Health Management Conference (PHM-Paris), Paris, France, 2019, pp. 114-120.

[4] W. Zhao, Y. Lv, X. Guo and J. Huo, "An Investigation on Early Fault Diagnosis Based on Naive Bayes Model," 2022 7th International Conference on Control and Robotics Engineering (ICCRE), Beijing, China, 2022, pp. 32-36.