Powertrain Modeling of an Electric Three-Wheeler with Regenerative Braking and Control Enhancement Using Gray Wolf Optimization

Abstract

The increasing demand for efficient and sustainable urban transportation has highlighted the need for advanced electric vehicle technologies. This study presents the development of a comprehensive powertrain model for an electric three-wheeler, incorporating a regenerative braking system and control enhancements using the Gray Wolf Optimization (GWO) algorithm. The proposed model addresses the critical challenge of maximizing energy efficiency in electric three-wheelers by integrating a boost converter into the regenerative braking system, enhancing energy recovery during deceleration.The powertrain model encompasses a detailed representation of motor dynamics, battery management, and regenerative braking mechanisms. The boost converter plays a pivotal role in elevating the voltage generated during braking, thereby optimizing the energy recapture process and increasing the state of charge (SOC) of the battery. To further refine the control system, the GWO algorithm is employed to optimize the parameters of the PID controller, leading to improved control accuracy and system stability.Simulation results reveal substantial improvements in vehicle performance, including enhanced energy efficiency, increased SOC, and extended driving range. The integration of the boost converter and the optimized control system significantly reduces energy loss during braking and ensures smoother vehicle operation. The GWO-optimized control strategies demonstrate superior performance compared to traditional methods, achieving better energy recovery rates and vehicle stability. This research provides a robust framework for advancing the powertrain design of electric three-wheelers, contributing to the development of more efficient and practical urban transportation solutions. The findings align with global objectives to reduce environmental impact and promote green transportation, underscoring the potential of the proposed model to support sustainable mobility initiatives.