Performance Enhancement of Electric Vehicle Powertrain Systems Using AI-Based Control

Abstract

The rapid growth of electric vehicles has intensified the demand for intelligent powertrain systems capable of delivering superior energy efficiency, enhanced driving performance, and greater operational reliability under diverse real-world conditions. Conventional rule-based control strategies often struggle to adapt to dynamic traffic environments, varying road gradients, fluctuating battery states, and changing driver behavior, leading to suboptimal energy utilization and reduced vehicle efficiency. This study proposes an artificial intelligence-based control framework for electric vehicle powertrain systems that integrates data-driven decision-making with adaptive motor, battery, and torque management to improve overall vehicle performance. The proposed approach continuously analyzes operational parameters, including battery state of charge, motor speed, vehicle acceleration, regenerative braking potential, thermal conditions, and driving patterns, enabling real-time optimization of power distribution across the propulsion system. By employing predictive learning mechanisms, the controller anticipates load variations and dynamically adjusts torque delivery, thereby minimizing unnecessary energy consumption while maintaining smooth acceleration and enhanced drivability. The intelligent control architecture also improves regenerative braking efficiency by optimizing energy recovery during deceleration without compromising passenger comfort or vehicle stability. Furthermore, adaptive thermal management contributes to prolonged battery life and improved motor efficiency by regulating temperature-sensitive operating conditions. Simulation based performance evaluation under urban, highway, and mixed driving cycles demonstrates measurable improvements in energy efficiency, acceleration response, battery utilization, and overall driving range when compared with conventional control methodologies. The findings indicate that AI-enabled powertrain management effectively reduces power losses, enhances traction performance under variable road conditions, and supports predictive maintenance through continuous monitoring of component health. Beyond performance improvements, the proposed framework contributes to sustainable transportation by maximizing energy utilization, reducing battery degradation, and extending the operational lifespan of critical powertrain components. The study highlights the practical significance of integrating artificial intelligence into next-generation electric mobility, where intelligent control systems can simultaneously address efficiency, reliability, safety, and user experience. The presented methodology offers a scalable foundation for future electric vehicle platforms and autonomous mobility applications, supporting the development of highly adaptive, energy-aware, and environmentally sustainable transportation systems capable of meeting evolving industrial and societal demands.