Enhanced Energy Management System (EMS) and motor health monitoring for electric vehicles: a machine learning approach

This paper proposes a machine learning-based framework for enhancing the Energy Management System (EMS) and motor health monitoring in electric vehicles (EVs). The EMS uses supervised learning models like Decision Tree, Support Vector Machine (SVM), and XGBoost to sort energy states based on real-time data like temperature, torque, load, and state-of-charge (SOC). The system also includes motor health monitoring by using an Isolation Forest algorithm to find faults without supervision based on changes in motor efficiency, current, torque, and temperature. XGBoost achieved the highest classification accuracy of 93% in predicting EMS control decisions, outperforming SVM and Decision Tree models. Battery health indicators such as State of Health (SoH) are incorporated as additional features to enhance system awareness; however, their direct impact on predictive accuracy is not evaluated in the present study. The suggested EMS framework also cut average energy use by 12.5% compared to traditional rule-based strategies and moved control within 2.8 seconds when a motor fault occurred. These results show that motor condition monitoring analytics could make electric vehicles more reliable, energy-efficient, and fault-tolerant.

Energy Management System; Motor Health Monitoring; Machine Learning; Battery Health; Motor Condition Mointoring.