A Constraint-Based Routing and Charging Methodology for Battery Electric Vehicles With Deep Reinforcement Learning

Electric vehicle route planning (EVRP) is generated from the collaborative operation of smart grids and intelligent transportation systems. It has become an essential issue for the widespread use of battery electric vehicles. However, the existed EVRP solutions are of either high computational complexity or low efficiency for large-scale problems. Towards such issues, we propose an efficient Deep Reinforcement Learning based methodology for constraint-based routing while simultaneously considering electric vehicles' charging policies. We design a two-layer model to find near-optimal solutions and handle the broad range of problem instances according to the rewards and the preset feasibility rules. The first layer is to approximate a sequence of consecutive actions in reality and correspondingly produce a minimum time-consuming feasible route without re-training for each new problem instance. The second layer is to generate a charging scheme along the previously generated feasible path. The proposed methodology is independent of the road network topology and the electric vehicles' types. Besides, the convergence of value function in the presented model for EVRP is studied. The experiment shows that our methodology outperforms the traditional ones in computation time with comparable solution quality. Moreover, the obtained model can be directly applied to treat other problem instances on various road networks without re-training procedures.