Intelligent Electric Drive Systems for Next Generation Electric Vehicles

¹ Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India.
² Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India.

^* Corresponding author: ku.AnjaliGoswami@kalingauniversity.ac.in

Abstract

The advanced Tesla electric vehicles utilize some of the most cutting-edge technologies to lead the industry. Innovative adaptive control and machine learning techniques are the main topics of this study, which aims to improve energy efficiency, torque distribution, and battery management via the development and implementation of intelligent electric drive systems (IEDS). These systems incorporate extensive predictive load forecasting and advanced analytics to assist with dependable performance under complex and variable operating scenarios, adaptive to electric-driven systems. They feature fault-tolerant structures for torque active delegation, aimed toward dependable performance across multiple operating scenarios. This allows for real-time adaptive torque control and have structures that can withstand failure. To provide dynamic load balancing and vehicle-to-grid (V2G) systems, IEDS complements smart charging bases. This research shows that hybrid artificial intelligence algorithms for battery management systems (BMS), digital twin-based cloud-edge optimization, predictive maintenance, and other optimization techniques may outperform existing optimization methods in terms of both energy and cost efficiency. Future electric vehicles may be modeled after this study’s framework, which prioritizes smart mobility infrastructure, grid integration, and environmental friendliness.