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 2 s 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.