Model and simulation of the fuzzy reliability assessment of the electric vehicle motor

The energy crisis has spurred the development of electric vehicles (EVs). As one of key components of EVs, the reliability of motor drive system directly affects the wide application of EVs. Due to the bad working environment and complex working mode, the reliability of the motor is more prominent....

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Bibliographic Details
Published in:Advances in mechanical engineering 2022-08, Vol.14 (8)
Main Authors: Zhou, Xuesheng, Yang, Jun, Wang, Jian, Li, Jingwei
Format: Article
Language:English
Subjects:
Algorithms
Bearing strength
Component reliability
Computer simulation
Electric vehicles
Failure modes
Fault trees
Hybrid systems
Neural networks
Permanent magnets
Reliability analysis
Reliability aspects
Synchronous motors
Working conditions
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Summary:The energy crisis has spurred the development of electric vehicles (EVs). As one of key components of EVs, the reliability of motor drive system directly affects the wide application of EVs. Due to the bad working environment and complex working mode, the reliability of the motor is more prominent. However, at present, there are few researches on the reliability of drive motor of EVs. Therefore, research on the reliability of electric vehicle motor drive system is of great significance. In this work, the reliability theory and prior motor reliability work were applied to the validity research of driving motor of EVs. Correlation studies were performed by using a permanent magnet synchronous motor. The failure modes of the driving motor and factors influencing reliability were examined considering the environment and motor to establish a fault tree model and analyze reliability weaknesses, including the winding insulation, bearings, and permanent magnets. The reliability of the motor was then modeled via MATLAB using a hybrid intelligent algorithm based on stochastic simulation and a neural network. The results showed that the model and the algorithm were effective. In addition, this work is helpful for electric vehicle designers to improve the reliability of electric vehicle motors.
ISSN:1687-8132
1687-8140
DOI:10.1177/16878132221120406