From V2X Preview to Powertrain Control: Coupled Eco-Driving and Predictive Energy Management for Connected Electrified Vehicles

Vehicle-to-everything (V2X) connectivity provides electrified vehicles with previews of traffic signals, road geometry, surrounding traffic, and route conditions, yet control benefit arises only when these data are converted into variables that can shape motion and powertrain decisions. This review presents a structured, framework-driven narrative synthesis organized along an information–motion–energy chain: external preview, control-oriented prediction, energy-aware speed planning, trip-level energy and state-of-charge scheduling, power-source allocation, cross-layer coordination, and staged validation. The reviewed studies are compared in terms of coupling depth, from traffic-layer optimization and sequential speed–energy management strategy (EMS) schemes to hierarchical/weakly coupled and joint/tightly coupled formulations. Across hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel cell electric vehicle/hybrid electric vehicle (FCEV/FCHEV) platforms, the information interface is broadly shared, whereas energy-replenishment, thermal, and component-health constraints require powertrain-specific formulations. The evidence base also shows a persistent maturity gap between algorithmic simulation and hardware or vehicle validation. Key needs are uncertainty-aware closed-loop design, physically interpretable model–data fusion, fallback control under information degradation, standardized cross-layer benchmarks, and staged validation that reports both control performance and evidence level.