HYBRID ELECTRIC VEHICLES: A COMPREHENSIVE STUDY OF MECHANICAL AND ELECTRICAL INTEGRATION

Abstract

Hybrid Electric Vehicles (HEVs) have become a central focus of sustainable transportation research, driven by the need to reduce emissions, enhance energy efficiency, and optimize multi-source power delivery. This comprehensive review evaluates recent advancements in mechanical–electrical integration, emphasizing the convergence of high-fidelity modeling, intelligent energy management, and predictive battery monitoring. A systematic methodology guided the selection and synthesis of contemporary studies, focusing on four core domains: multi-objective energy management optimization, model predictive control (MPC), reinforcement learning (RL)-based adaptive strategies, and advanced battery state-of-charge (SOC) estimation. Results indicate that multi-objective optimization frameworks significantly enhance drivetrain efficiency, while MPC remains indispensable for predictive torque-split control despite computational limitations. RL-based controllers demonstrate superior adaptability to dynamic driving environments, achieving measurable gains in energy utilization and control robustness. Additionally, hybrid ECM–ML and EIS-driven battery models deliver state-of-the-art SOC estimation accuracy, reinforcing the importance of precise battery monitoring for ensuring long-term reliability and thermal stability. The discussion highlights persistent challenges including thermal constraints, real-world deployment barriers, and the need for integrated digital twin ecosystems while also outlining key opportunities for future HEV innovation. Overall, this review establishes a cohesive understanding of mechanical–electrical co-design trends and provides strategic insights to guide next-generation hybrid powertrain development.