Electric vehicle routing optimization under 3D electric energy modeling

In logistics transportation, the electric vehicle routing problem (EVRP) is researched widely in order to save vehicle power expenditure, reduce transportation costs, and improve service quality. The power expenditure model and routing algorithm are essential for resolving EVRP. To align the routing schedule more reasonable and closer to reality, this paper employs a three-dimensional power expenditure model to calculate the power expenditure of EVs. In this model, the power expenditure of the EVs during the process of going up and downhill is considered to solve the routing schedule of logistics transportation in mountainous areas. This study combines Q-learning and the Re-insertion Genetic Algorithm (Q-RIGA) to design EV routes with low electricity expenditure and reduced transportation costs. The Q-learning algorithm is used to improve route initialization and obtain high-quality initial routes, which are further optimized by RIGA. Tested in a collection of randomly dispersed customer groups, the advantages of the proposed method in terms of convergence speed and power expenditure are confirmed. The three-dimensional power expenditure model with consideration of elevation is used to conduct simulation experiments on the distribution example of Sanlian Dairy in Guizhou to verify that the improved model features broader application and higher practical value.