Modeling and Control of Electric Vehicle Powertrains for Enhanced Efficiency

Abstract

Driven by the growing need for sustainable transportation solutions over the last decade, significant progress has been made in electric vehicle (EV) powertrain technologies. In this study, a holistic model and a control strategy for EV powertrain efficiency optimization are introduced. The development of a dynamic mathematical model of the EV powertrain, comprising of the motor, inverter and energy storage systems, to simulate performance for different driving cycles. More sophisticated supervisory control techniques, like model predictive control (MPC) and adaptive fuzzy logic, has been applied for the improvement of energy management and operational performance. Simulation and experimental results show that the energy-aware control approach has resulted in improvements up to 15% in energy utilization when compared to classical control methods. The suggested power arbitrating technique gives unequalled power dispersion among elements, cuts down vitality loses, and broaden battery lifespan while not relinquishing vehicle execution and driving comfort. It also assesses the role of regenerative braking and thermal management on overall efficiency. The results indicate it is possible to optimize for both performance and efficiency using intelligent control systems, which could enhance the viability and sustainability of EVs in the future mobility. Our research offers a scalable framework for the design and control of next-generation EV powertrains that can be beneficial for both automakers and researchers.