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.

Automatic Guided Vehicle Scheduling in Automated Container Terminals Based on a Hybrid Mode of Battery Swapping and Charging

“…Due to the NP-hard feature of the problem, the CPLEX solution time increases exponentially with the number of tasks and AGVs, which makes it difficult to obtain effective results in a reasonable time. Referring to the experimental scheme of Zhuang [28], T real was set to be the running time of the CPLEX solution model, and the maximum solution time T max allowed by the corresponding algorithms was set according to the scale size of the arithmetic example.…”

“…That is, the AGV k battery swapping threshold must be greater than or equal to the remaining power of the AGV k when completing task i minus the no-load energy consumption of AGV k from the end position of task i to the start position of task j. Constraints ( 23) to (28) represent the range of values of the decision variables.…”

“…Ferro et al [27] considered the selection of different types of charging stations, and developed a mixed-integer programming model, which was used to minimize the traveling distance cost and energy costs. Kumar et al [28] studied the electric vehicle path problem with time windows, modeling the problem with battery swapping, partial charging, charging flexibility, and energy prices for different charging levels. The results related to electric vehicle scheduling verify the effectiveness of the hybrid mode of battery swapping and charging and provide an important reference for the optimization of AGV scheduling in ACTs under the battery swapping and charging mode.…”