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Article

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

1
Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
2
Department of Energy and Control, Normandy University, 76800 Rouen, France
*
Author to whom correspondence should be addressed.
Academic Editor: Wencheng Guo
Energies 2021, 14(20), 6599; https://doi.org/10.3390/en14206599
Received: 6 July 2021 / Revised: 27 September 2021 / Accepted: 28 September 2021 / Published: 13 October 2021
(This article belongs to the Special Issue Smart Energy Management and Power Electronic Systems)
Diagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning. An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software. It is also revealed how deep learning contributes to the fault diagnosis of EVs. The simulation and practical results confirm that higher accuracy in the fault diagnosis is obtained by applying the LSTM. View Full-Text
Keywords: artificial neural network (ANN); data analytics; deep learning; electric vehicles; fault diagnosis; long short-term memory (LSTM) artificial neural network (ANN); data analytics; deep learning; electric vehicles; fault diagnosis; long short-term memory (LSTM)
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MDPI and ACS Style

Kaplan, H.; Tehrani, K.; Jamshidi, M. A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications. Energies 2021, 14, 6599. https://doi.org/10.3390/en14206599

AMA Style

Kaplan H, Tehrani K, Jamshidi M. A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications. Energies. 2021; 14(20):6599. https://doi.org/10.3390/en14206599

Chicago/Turabian Style

Kaplan, Halid, Kambiz Tehrani, and Mo Jamshidi. 2021. "A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications" Energies 14, no. 20: 6599. https://doi.org/10.3390/en14206599

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