Machine learning-based energy management system for electric vehicles with BLDC motor integration

Abstract

This paper proposes a machine learning-based energy management system for electric vehicles with BLDC motor integration. Efficient energy management is essential for improving the performance, range, and reliability of electric vehicles (EVs), particularly those powered by brushless DC (BLDC) motors. Traditional energy management systems (EMS), such as rule-based and fuzzy logic controllers, often lack the adaptability required for dynamic driving conditions and optimal energy distribution. This paper presents a machine learning (ML)-based EMS framework tailored for EVs equipped with BLDC motors, aiming to enhance system responsiveness and energy efficiency. ML algorithms, including decision trees, random forests, support vector machines (SVMs), and XGBoost, are trained on diverse datasets that reflect varying load demands, driving cycles, and battery state-of-charge (SOC) levels. The proposed EMS is modeled and validated in Python programming to simulate realistic EV operating scenarios. Simulation results indicate that the ML-based EMS outperforms conventional methods by achieving up to 15% energy savings, reducing battery stress, and maintaining smoother SOC transitions. These findings highlight the potential of ML-driven strategies for creating adaptive, intelligent EMS solutions in next-generation BLDC motor-based EVs.