Research on fault diagnosis model of electric vehicle based on learning algorithm

Jumei Wang; Tao Fang

doi:10.1117/12.3025439

28 February 2024 Research on fault diagnosis model of electric vehicle based on learning algorithm

Jumei Wang, Tao Fang

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130713J (2024) https://doi.org/10.1117/12.3025439
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

In order to improve the efficiency and accuracy of fault diagnosis of electric vehicle drive motor, this paper proposes a study on fault diagnosis model of electric vehicle based on learning algorithm. The system uses sensors to collect the data of motor operation state, and after fuzzy processing, the knowledge base and database are mobilized according to fuzzy rules to diagnose the motor operation state. The test results show that the accuracy of the system in motor fault diagnosis is 99.8%, and the response time is controlled within 200μs, which greatly improves the performance of the traditional system. In the four tests, the fault diagnosis time was less than 200μs, which was about 100μs shorter. In order to test the accuracy of system fault diagnosis, 1000 simulation tests were carried out in this test. The test results show that only two diagnosis results are deviated, and the problem appears in the speed data, which leads to the wrong simulation diagnosis results. Therefore, the current diagnostic accuracy of the system is 99.9%, which is significantly improved compared with the traditional diagnostic system. The validity and reliability of the method proposed in this paper are verified.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jumei Wang and Tao Fang "Research on fault diagnosis model of electric vehicle based on learning algorithm", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130713J (28 February 2024); https://doi.org/10.1117/12.3025439

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