Context-Aware and Energy-Driven Route Optimization for Fully Electric Vehicles via Crowdsourcing

Abstract

Route planning for fully electric vehicles (FEVs) must take energy efficiency into account due to limited battery capacity and time-consuming recharging. In addition, the planning algorithm should allow for negative energy costs in the road network due to regenerative braking, which is a unique feature of FEVs. In this paper, we propose a framework for energy-driven and context-aware route planning for FEVs. It has two novel aspects: 1) It is context aware, i.e., the framework has access to real-time traffic data for routing cost estimation; and it is energy driven, i.e., both time and energy efficiency are accounted for; which implies a biobjective nature of the optimization. In addition, in the case of insufficient energy on board, an optimal detour via recharge points is computed. Our main contributions to address these issues can be highlighted as follows: A vehicle-to-vehicle (V2V) communication protocol is proposed to realize the context awareness, and we replace the original biobjective form of optimality with two single-objective forms and propose a constrained A* ( CA*) algorithm to find the solutions. The algorithm maintains a Pareto front while it confines its search by energy constraints. The best recharging detour can be also found using the algorithm. We first compared the performance of the CA* algorithm with other algorithms. We then evaluate the impact of the context awareness on road traffic by simulations using a realistic road network regarding different forms of optimality. Finally, we show that the CA* algorithm can effectively produce optimal recharging detours.