Battery and energy management system for BLDC motor driven electric vehicles with intelligent control and machine learning techniques

Abstract

This research paper presents an improved Battery and Energy Management System (BEMS) designed for Brushless DC (BLDC) motor-driven electric vehicles using smart control approaches and machine learning. By including predictive ML models such as Decision Trees, Support Vector Machines (SVM), and XGBoost for precise assessment of battery state-of-charge (SOC) and real-time energy allocation, the system seeks to optimise motor control and battery performance. Dynamically controlling power flow depending on SOC, temperature, and driving circumstances, a smart battery and energy management system is created. Comparison with traditional EMS methods reveals notable gains in energy economy, temperature management, battery life, and motor response. The combination of ML and smart control shows a strong and flexible system for improving the general performance and sustainability of electric vehicle powertrains.