ABSTRACT

Sudden power loss is a great threat to electric vehicles, which could lead to serious accidents and damages. This study developed and verified a power loss risk assessment method using naturalistic driving experiment data of electric vehicles (EVs). The characteristic indexes related to the power loss were extracted by binary logistic regression. It was determined that total battery voltage, motor temperature, motor controller temperature, DC/DC temperature, and maximum battery temperature are related to power loss risk. Power loss risk of EVs was evaluated using the entropy weight method, which is a comprehensive method that can integrate indicators. The risk evaluation score is obtained by integrating all indicators related to the risk of power loss in the regression analysis. One thousand data points containing alarms are input for verification. The estimated model has a high detection accuracy of 82%.

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Go to CICTP 2023
CICTP 2023
Pages: 2930 - 2940

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Published online: Aug 30, 2023

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Zongrui Xie [email protected]
1Intelligent Transportation Systems Research Center, Wuhan Univ. of Technology, Wuhan, China. Email: [email protected]
2Intelligent Transportation Systems Research Center, Wuhan Univ. of Technology, Wuhan, China. Email: [email protected]
Naikan Ding [email protected]
3Intelligent Transportation Systems Research Center, Wuhan Univ. of Technology, Wuhan, China. Email: [email protected]
4Intelligent Transportation Systems Research Center, Wuhan Univ. of Technology, Wuhan, China. Email: [email protected]
Shaopeng Li [email protected]
5Intelligent Transportation Systems Research Center, Wuhan Univ. of Technology, Wuhan, China. Email: [email protected]
Hongxia Feng [email protected]
6Intelligent Transportation Systems Research Center, Wuhan Univ. of Technology, Wuhan, China. Email: [email protected]

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