Artificial intelligence-enabled energy optimization in electric and hybrid vehicles: towards sustainable and smart transportation systems

The rapid electrification of transportation demands advanced energy management strategies capable of improving efficiency, extending battery lifetime, and enhancing system level sustainability. This review provides a comprehensive and quantitative assessment of artificial intelligence enabled energy optimization techniques for electric and hybrid vehicles, with emphasis on their integration into smart and renewable energy systems. We systematically analyze machine learning, deep learning, reinforcement learning, and hybrid model-based frameworks across vehicle level and grid interactive applications. Reported literature indicates energy consumption reductions of 3 to 20% (median: 10.2%, IQR: 6.8–15.4%; estimated I 2 = 78%, high heterogeneity; results represent qualitative performance envelopes rather than pooled effect sizes), fuel savings in hybrid architectures up to 22% (median: 12.5%, IQR: 8.1–18.3%; I 2 > 75%), regenerative energy utilization improvements of 5 to 25%, and state of charge estimation error reductions of up to 40% compared to conventional rule-based strategies. Beyond vehicle level optimization, we examine vehicle to grid coordination, renewable aligned smart charging, multi energy system interaction, and distributed fleet optimization. Tradeoffs among computational complexity, real time feasibility, battery degradation, and lifecycle emissions are critically evaluated using normalized performance metrics. The analysis demonstrates that hybrid AI and physics informed approaches offer superior robustness and scalability for real world deployment. Finally, key research gaps are identified in uncertainty quantification, degradation aware control, grid carbon intensity aware charging, and large-scale validation under real driving conditions. This work provides an integrated energy systems perspective to guide the development of intelligent, low carbon, and grid compatible transportation solutions.