This paper investigates advanced and learning-inspired torque-allocation strategies for quad-motor electric vehicles (EVs) within a sustainable and digitally oriented control perspective. A simplified yet physically consistent longitudinal model is coupled with four representative allocators: a heuristic baseline, a quadratic-programming MPC formulation, an AI-inspired rule-based scheme, and a Hybrid approach combining model-based and data-driven principles. Their behaviour is evaluated on WLTP3, US06, and UDDS cycles using key indicators including energy demand, CO2-equivalent impact, driveline smoothness, SoC evolution, and computation time. Results show that the Hybrid strategy achieves near-AI energy savings while preserving MPC-grade stability, yielding up to 15% reductions in energy and CO2 per kilometer. The study contributes to the growing field of green digital control, demonstrating how reliability from physics-based modelling and adapt-ability from lightweight AI rules can jointly enhance efficiency and durability in modern EV platforms.