基于电动车特性优化配送速度与路径，降低能耗17.16%。

The abundance of materials and the development of the economy have led to the flourishing of the logistics industry, but have also caused certain pollution. The research on GVRP (Green vehicle routing problem) for planning vehicle routes during transportation to reduce pollution is also increasingly developing. Further exploration is needed on how to integrate these research findings with real vehicles. This paper establishes an energy consumption model using real electric vehicles, fully considering the physical characteristics of each component of the vehicle. To avoid the distortion of energy consumption models affecting the results of route planning. The energy consumption model also incorporates the effects of vehicle start/stop, speed, distance, and load on energy consumption. In addition, a load first speed optimization algorithm was proposed, which selects the most suitable speed between every two delivery points while planning the route. In order to further reduce energy consumption while meeting the time window. Finally, an improved Adaptive Genetic Algorithm is used to solve for the most energy-efficient route. The experiment shows that the results of using this speed optimization algorithm are generally more energy-efficient than those without using this algorithm. The average energy consumption of constant speed delivery at different speeds is 17.16% higher than that after speed optimization. Provided a method that is closer to reality and easier for logistics companies to use. It also enriches the GVRP model.