<p>A hierarchical control strategy for global energy flow optimization using navigation information (destination and average vehicle speed) is proposed to improve the overall fuel economy of plug-in hybrid vehicles. This method establishes a vehicle energy consumption estimation model and an equivalent specific fuel consumption model based on big-data learning in cloud controller. In the vehicle controller, an energy management strategy with the minimum equivalent fuel consumption is adopted to optimize the vehicle’s hybrid operation mode and the minimum limit of power generation torque, regulating vehicle power and fuel/electricity energy flow throughout the entire operating range. The effectiveness of the proposed strategy was validated by comparing the vehicle simulation using the road navigation speeds and the actual real-time vehicle speeds. The results indicate that this method can optimize the vehicle’s hybrid mode and prioritize electricity usage under different initial electricity levels while allowing the engine to operate under more economical operating conditions in real-time, reducing vehicle fuel consumption and CO₂ emissions. Moreover, it is more adaptable to changes in on-road driving routes, initial battery levels, and road conditions than conventional global energy management strategies.</p>