Department of Electrical and Electronic Engineering, Green University of Bangladesh, Dhaka, Bangladesh
Mohammed Arfat Raihan Chowdhury
Department of Logistics and Supply Chain Management, Division of Engineering and Decision Science, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3- $\phi $ inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3- $\phi $ inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.
