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Communication Dans Un Congrès Année : 2019

An Automatic Fault Detection and Localization Strategy for Switched Reluctance Machine Open-Circuit Fault in EVs Applications

Résumé

In recent years, urban electric vehicles have experienced significant growth in acquisition on the automotive market. Because of this growth and their use, these vehicles would be exposed to faults during driving causing emergency stops. A maintenance intervention is necessary to identify these faults. Among the faults that can occur on the vehicle, we are particularly interested in the electrical faults. The objective of this paper is to detect and locate automatically the open circuit faults of the electronic power converter associated to four phases 8/6 Switched Reluctance Machine used in an electric vehicle drivetrain. The developed methodology is based on spectral analysis using the FFT algorithm with a periodic sliding window for total reference torque provided by the velocity controller and measured current of each phase. Finally, the development methodology is validated on the simulator of the electric vehicle drivetrain creating an open circuit fault in any phase of the converter / Switched Reluctance Machine with a vehicle load torque. Moreover, this methodology could be extended to detect more than one open circuit fault. However, this strategy could be beneficial to implement it in the control unit of electric vehicles to identify and to locate an open circuit fault automatically. Finally, the development methodology is validated on the simulator of the electric vehicle drivetrain creating an open circuit fault in any phase of the converter / Switched Reluctance Machine with a vehicle load torque.
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hal-04502950 , version 1 (13-03-2024)

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Yakoub Saadi, Rabia Sehab, Ahmed Chaibet, Mousaa Boukhnifer, Demba Diallo. An Automatic Fault Detection and Localization Strategy for Switched Reluctance Machine Open-Circuit Fault in EVs Applications. 2019 International Conference on Control, Automation and Diagnosis (ICCAD 2019), Jul 2019, Grenoble, France. ⟨10.1109/iccad46983.2019.9037933⟩. ⟨hal-04502950⟩
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