Machine Learning–Enabled Fault Detection And Classification For Enhancing Electric Vehicle Performance And Security
DOI:
https://doi.org/10.46647/rdems0205014Keywords:
Electric Vehicles (EV), Machine Learning, Fault Detection, Fault Classification, Predictive Maintenance, Anomaly Detection, Battery Management Systems (BMS), Electric Drive Systems, Data-Driven Modeling, Condition Monitoring, Cyber-Physical Systems, Reliability Analysis, Intelligent Diagnostics, Deep Learning.Abstract
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.