A mat‐heuristics approach for electric vehicle route optimization under multiple recharging options and time‐of‐use energy prices

Abstract

SummaryTraveling has contributed a lot to the evolution of mankind. Today, electric vehicles (EVs) are being preferred due to their greater efficiency, comfort, and environment‐friendly qualities. The EVs' contribution to future mobility is projected to rise exponentially in years to come. To make this innovative technology more successful, there is a dire need to install a sufficient number of charging stations (CSs). As the EVs are limited by their cruising range, they require multiple recharging to cover long distances (especially in the case of logistics delivery services). Thus, there is a great need to develop an efficient and cost‐effective EV route optimization approach considering multiple recharging options and time‐of‐use (ToU) energy prices. In this regard, a novel mat‐heuristic approach named (firefly with ant colony algorithm) has been proposed to solve the problem of EVRPTW (electric vehicle routing problem with time windows) incorporating detailed modeling of multiple charging flexibility (i.e., battery swapping, partial recharge, and different charging levels) and ToU energy prices. Our proposed approach aims to minimize the total cost of traveling, which is highly influenced by the cost of recharging. Ant colony algorithm (ACA) serves as the basic optimization framework in the proposed approach, while the firefly approach explores hitherto unexplored solution space and avoids local optima. The computation performance of the proposed approach is compared with existing state‐of‐the‐art similar domain approaches such as variable neighborhood search (VNS) and ant colony optimization using local search (ACO‐LS) which has average deviation of nearly 20%–25% from with optimal solution achieved by the proposed . The proposed approach yields a near‐optimal solution with a faster convergence rate (approximately 50%) compared to other existing approaches. Moreover, the multiple recharging options modeled in our proposed approach justify their significance in terms of cost‐effectiveness for most scenarios.