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

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%.