An Optimal Charging Solution for Commercial Electric Vehicles

• Published in: IEEE access Vol. 10; pp. 46162 - 46175
• Main Authors: Al-Hanahi, Bassam, Ahmad, Iftekhar, Habibi, Daryoush, Pradhan, Pravakar, Masoum, Mohammad A. S.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Charging; Charging stations; Commercial vehicles; Companies; Costs; Electric power demand; Electric vehicles; Electrical networks; electrification; Government regulations; greenhouse gas emissions; Incentives; Logistics; Optimization; Optimization models; Routing; Schedules; Time of use; transportation; vehicle routing; vehicle to grid
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3171048

New government regulations and incentives promote the deployment of commercial electric vehicles to reduce carbon emissions from gasoline-fueled vehicles. For commercial electric vehicles (CEVs) operating in a fleet, charging processes are often performed at the depot where they begin and end their daily driving cycles, as well as at public stations on their routes. With the large penetration of CEVs in depots, simultaneous charging increases peak demand, which in turn impacts the electric network and increases the demand cost of a facility. These depot charging conditions influence the charging schedules of CEVs along their routes and the total service cost of logistic companies. This paper investigates optimal charging problems for CEVs that are supported by charging stations at depot and on-route public charging stations. The optimal charging and routing problems of CEVs are modelled as an optimization problem and relevant solutions are provided. The charging variants considered in the optimization model are peak demand of depot charging, time of use tariffs during the day, partial recharging, waiting times and characteristics of public stations. The results indicate the effectiveness of the developed algorithm in achieving optimal routes that maximize the benefits of logistics companies provided all constraints are satisfied.