Development of a Fault-Diagnosis System through the Power Conversion Module of an Electric Vehicle Fast Charger

Sang-Jun Park, Woo-Joong Kim, Byeong-Su Kang, Sung-Hyun Jang, Yeong-Jun Choi and Young-Sun Hong
Additional contact information
Sang-Jun Park: Sustainable Technology and Wellness R&D Group, Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, Jeju-do, Korea
Woo-Joong Kim: Sustainable Technology and Wellness R&D Group, Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, Jeju-do, Korea
Byeong-Su Kang: Sustainable Technology and Wellness R&D Group, Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, Jeju-do, Korea
Sung-Hyun Jang: Sustainable Technology and Wellness R&D Group, Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, Jeju-do, Korea
Yeong-Jun Choi: Department of Electrical Engineering, Jeju National University, 102 Jejudaehak-ro, Jeju-si 63243, Jeju-do, Korea
Young-Sun Hong: Sustainable Technology and Wellness R&D Group, Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, Jeju-do, Korea

Energies, 2022, vol. 15, issue 14, 1-11

Abstract: The supply of electric vehicles (EVs), charging infrastructure, and the demand for chargers are rapidly increasing owing to global low-carbon and eco-friendly policies. As the maintenance of charging infrastructure varies depending on the manufacturer, fault detection and maintenance cannot be conducted promptly. Consequently, user inconvenience increases and becomes an obstacle to EV distribution. Recognizing charger failure after occurrence is a management method that is not economically effective in terms of follow-up. In this study, a data collection system was developed to diagnose EV fast-charger failure remotely in advance. The power module failure-prediction and management system consists of an AC sensor, DC sensor, temperature and humidity sensor, communication board, and data processing device. Furthermore, it was installed inside the fast charger. Four AC inputs, four DC outputs, and temperature and humidity data were collected for 12 months. Using the collected data, the power conversion efficiency was calculated and the power module status was diagnosed. In addition, a multilayer perceptron neural network was used as an algorithm for training the classification model. Charging patterns according to normal and failure were trained and verified. Based on results, the pre-failure diagnosis system demonstrated an accuracy of 97.2%.

Keywords: fast charger; power module; power conversion efficiency; maintenance; diagnostic method; current sensor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/14/5056/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/14/5056/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:14:p:5056-:d:860211

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().