CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine

This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and pred...

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Autores principales:	Gabriel Rojas-Dueñas, Jordi-Roger Riba, Manuel Moreno-Eguilaz
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	power converters electric vehicles fault diagnosis accelerated aging tests artificial neural networks Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/a147b61670494649810cab3672cb5618
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spelling	oai:doaj.org-article:a147b61670494649810cab3672cb56182021-11-11T19:05:49ZCNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine10.3390/s212170791424-8220https://doaj.org/article/a147b61670494649810cab3672cb56182021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7079https://doaj.org/toc/1424-8220This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and specifically of the electrolytic capacitors, in order to avoid malfunctioning and failures, since it is known they have the highest failure rates among power converter components. To this end, accelerated aging tests of the electrolytic capacitors are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM model that is capable of predicting the future values of the capacitance and the equivalent series resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful life of the device, thus, increasing the reliability of the system and ensuring an adequate operating condition of the electric motor.Gabriel Rojas-DueñasJordi-Roger RibaManuel Moreno-EguilazMDPI AGarticlepower converterselectric vehiclesfault diagnosisaccelerated aging testsartificial neural networksChemical technologyTP1-1185ENSensors, Vol 21, Iss 7079, p 7079 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	power converters electric vehicles fault diagnosis accelerated aging tests artificial neural networks Chemical technology TP1-1185
spellingShingle	power converters electric vehicles fault diagnosis accelerated aging tests artificial neural networks Chemical technology TP1-1185 Gabriel Rojas-Dueñas Jordi-Roger Riba Manuel Moreno-Eguilaz CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine
description	This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and specifically of the electrolytic capacitors, in order to avoid malfunctioning and failures, since it is known they have the highest failure rates among power converter components. To this end, accelerated aging tests of the electrolytic capacitors are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM model that is capable of predicting the future values of the capacitance and the equivalent series resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful life of the device, thus, increasing the reliability of the system and ensuring an adequate operating condition of the electric motor.
format	article
author	Gabriel Rojas-Dueñas Jordi-Roger Riba Manuel Moreno-Eguilaz
author_facet	Gabriel Rojas-Dueñas Jordi-Roger Riba Manuel Moreno-Eguilaz
author_sort	Gabriel Rojas-Dueñas
title	CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine
title_short	CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine
title_full	CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine
title_fullStr	CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine
title_full_unstemmed	CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine
title_sort	cnn-lstm-based prognostics of bidirectional converters for electric vehicles’ machine
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a147b61670494649810cab3672cb5618
work_keys_str_mv	AT gabrielrojasduenas cnnlstmbasedprognosticsofbidirectionalconvertersforelectricvehiclesmachine AT jordirogerriba cnnlstmbasedprognosticsofbidirectionalconvertersforelectricvehiclesmachine AT manuelmorenoeguilaz cnnlstmbasedprognosticsofbidirectionalconvertersforelectricvehiclesmachine
_version_	1718431663209316352

CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine

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