Energy-Efficient Route and Velocity Planning for Electric Vehicles: A Hierarchical Eco-Driving Framework Integrating Traffic and Road Information

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The growing demand for decarbonization, coupled with the development of intelligent transportation systems (ITS), has driven the emergence of eco-driving technologies for electric vehicles (EVs). However, existing eco-driving technologies rarely integrate path and velocity planning while neglecting macro traffic flow and environmental impacts, resulting in less practical and less precise planning outcomes. Therefore, this study proposes a hierarchical eco-driving model that establishes a high-dimensional system incorporating macro traffic flow, micro vehicle model, and road environments. First, a traffic network model is constructed based on the real road topology. Next, a high-precision vehicle energy consumption model and a database of typical driving cycles are established to calculate the edge costs of the road network. Then, an energy-efficient route is efficiently planned using the proposed multi-heuristic A* algorithm. Finally, based on the route information from the upper level, along with traffic, kinematic, and road information, a convex optimization algorithm is employed to achieve accurate and efficient velocity planning. Experimental results demonstrate that the proposed method computes in less than 2s for most scenarios and can effectively save energy and time by over 10%. The proposed framework offers a new solution for eco-driving and has significant practical implications.