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

Author

Listed:
  • Halid Kaplan

    (Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Kambiz Tehrani

    (Department of Energy and Control, Normandy University, 76800 Rouen, France)

  • Mo Jamshidi

    (Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

Abstract

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.

Suggested Citation

  • Halid Kaplan & Kambiz Tehrani & Mo Jamshidi, 2021. "A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications," Energies, MDPI, vol. 14(20), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6599-:d:655252
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    References listed on IDEAS

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    1. Zhong, Shengyuan & Wang, Xiaoyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Wang, Yongzhen & Deng, Shuai & Zhu, Jiebei, 2021. "Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating," Applied Energy, Elsevier, vol. 288(C).
    2. Mahdi Bayati & Mehrdad Abedi & Maryam Farahmandrad & Gevork B. Gharehpetian & Kambiz Tehrani, 2021. "Important Technical Considerations in Design of Battery Chargers of Electric Vehicles," Energies, MDPI, vol. 14(18), pages 1-20, September.
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    Cited by:

    1. Jing Xu & Ren Zhang & Yangjun Wang & Hengqian Yan & Quanhong Liu & Yutong Guo & Yongcun Ren, 2022. "Assessing China’s Investment Risk of the Maritime Silk Road: A Model Based on Multiple Machine Learning Methods," Energies, MDPI, vol. 15(16), pages 1-15, August.
    2. Jikai Bi & Jae-Cheon Lee & Hao Liu, 2022. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics," Energies, MDPI, vol. 15(7), pages 1-24, March.

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