Intelligent Electrified Mobility Systems: AI-Based Energy Management, Battery Health Prediction, and Adaptive Control for Electric Vehicles

Abstract

The rapid adoption of electric vehicles (EVs) has highlighted the need for intelligent systems that optimize performance, extend battery life, and ensure sustainable mobility. This study investigated AI-based energy management, battery health prediction, and adaptive control strategies for electrified mobility systems. A hybrid approach integrating machine learning, reinforcement learning, and predictive analytics was employed to monitor real-time driving conditions, forecast battery state-of-health (SoH) and remaining useful life (RUL), and dynamically adjust energy distribution. The methodology involved simulation-based evaluations across urban, highway, and mixed driving cycles to assess energy efficiency, system responsiveness, and predictive accuracy. Results demonstrated that AI-driven energy management significantly reduced energy losses during acceleration and deceleration, while predictive models accurately anticipated battery degradation, enabling proactive maintenance interventions. Adaptive control mechanisms improved vehicle stability, optimized load distribution, and minimized battery stress during dynamic driving scenarios. Comparative analysis indicated that AI-based systems outperformed conventional rule-based strategies in terms of efficiency, reliability, and scalability. These findings underscore the potential of intelligent electrified mobility systems to enhance operational performance, prolong battery lifespan, and support sustainable transportation solutions. Future implementations are recommended to integrate explainable AI techniques and real-world validation to further improve transparency, reliability, and adoption. Overall, the study establishes a framework for AI-enabled EV systems, highlighting their transformative role in achieving energy-efficient, adaptive, and resilient electrified mobility.