Adaptive Control Strategy for Autonomous Electric Vehicle Energy Consumption Optimization

Abstract

The rapid advancement of autonomous driving technologies and electric mobility systems has increased the need for intelligent energy management strategies capable of improving vehicle efficiency, driving performance, and battery utilization. This study presents an adaptive control strategy for autonomous electric vehicle energy consumption optimization to enhance driving efficiency, reduce power losses, and extend battery operating range under dynamic driving conditions. The proposed framework integrates real-time vehicle monitoring, intelligent control algorithms, machine learning techniques, and energy management systems to optimize power distribution and driving behavior in autonomous electric vehicles. A data-driven methodology was implemented in which parameters such as vehicle speed, acceleration, battery state of charge, traffic conditions, road gradients, and environmental factors were continuously analyzed to enable adaptive energy control. Advanced optimization techniques and predictive control algorithms were employed to dynamically regulate propulsion, regenerative braking, and power allocation strategies based on real-time operational requirements. Simulation and analytical results demonstrated substantial reductions in energy consumption, battery degradation, and unnecessary power losses compared with conventional fixed-control energy management approaches. The adaptive control framework also improved driving stability, energy recovery efficiency, and overall vehicle performance under varying traffic and road conditions. Furthermore, the intelligent optimization strategy enhanced battery lifespan and supported efficient route-based energy planning for autonomous mobility applications. The study concludes that adaptive control-based energy optimization provides an effective and scalable solution for improving the operational efficiency and sustainability of autonomous electric vehicles. The findings contribute to the advancement of intelligent transportation systems through enhanced energy-aware autonomous driving, optimized battery management, and sustainable electric mobility infrastructure development.