A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications
| Autor: | Kambiz, Tehrani, Kaplan, Halid, TEHRANI, KAMBIZ, Jamshidi, Mo |
|---|---|
| Přispěvatelé: | IRSEEM POLE AUTOMATIQUE ET SYSTÈME |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Control and Optimization business.product_category Computer science Feature extraction Real-time computing Energy Engineering and Power Technology Context (language use) 02 engineering and technology Hardware_PERFORMANCEANDRELIABILITY Fault (power engineering) [SPI]Engineering Sciences [physics] Software Electric vehicle 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering MATLAB data analytics Engineering (miscellaneous) computer.programming_language electric vehicles [PHYS]Physics [physics] Renewable Energy Sustainability and the Environment business.industry Deep learning long short-term memory (LSTM) 020208 electrical & electronic engineering deep learning artificial neural network (ANN) fault diagnosis Data analysis 020201 artificial intelligence & image processing Artificial intelligence business computer Energy (miscellaneous) |
| Zdroj: | Energies, Vol 14, Iss 6599, p 6599 (2021) Energies; Volume 14; Issue 20; Pages: 6599 Energies Energies, MDPI, 2021, 14 (20), pp.6599. ⟨10.3390/en14206599⟩ |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en14206599⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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