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A Detailed Reliability Study of the Motor System in Pure Electric Vans by the Approach of Fault Tree Analysis

Lookup NU author(s): Dr Wenxian Yang

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Attributing to the unique feature of zero carbon emission, electric vehicles (EVs) are attracting increasing interest in recent years, but their reliability, particularly the reliability of their critical components, is still a matter of concern today. In order to address this issue, much effort has been made before to assess the reliability of drive motor in the EVs. However, drive motor and motor controller are logically integrated and requested to work as one system in the EVs. In contrast to the individual reliability analysis of them, the combined assessment of the two parts can provide a more reliable prediction to the reliability of the entire motor system. Moreover, both drive motor and motor controller are composed of multiple components. The structure, type, and characteristics of these components may affect the reliability of the motor system as well. But these issues have not been considered in the previous research. In order to fill this gap of knowledge, the reliability of the entire motor system of pure electric vans that includes bothdrive motor and motor controller is investigated in this paper. In the research, the theoretical failure rates ofsubassemblies and components in drive motor and motor controller are predicted. Then based on thefailure rate prediction results, the reliability of the entire motor system (comprising both drive motor andmotor controller) is assessed. Based on the assessment results, some interesting conclusions with respect tothe most vulnerable subassemblies and components in the entire motor system and the potential disadvantageof existing reliability research are finally obtained. It is deemed that these new findings will be of greatsignificance to the future reliability design and maintenance of pure electric vans.


Publication metadata

Author(s): Shu X, Guo Y, Yang W, Wei K, Zhu Y, Zou H

Publication type: Article

Publication status: Published

Journal: IEEE Access

Year: 2020

Volume: 8

Pages: 5295-5307

Online publication date: 31/12/2019

Acceptance date: 20/12/2019

Date deposited: 17/01/2020

ISSN (electronic): 2169-3536

Publisher: IEEE

URL: https://doi.org/10.1109/ACCESS.2019.2963197

DOI: 10.1109/ACCESS.2019.2963197


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