
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
An Optimal Charging Solution for Commercial Electric Vehicles

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.
- Utah Valley University United States
- Edith Cowan University Australia
- Utah Valley University United States
- Edith Cowan University Australia
transportation, Electric vehicles, vehicle to grid, greenhouse gas emissions, Electrical and Computer Engineering, 333, TK1-9971, Engineering, electrification, Electrical engineering. Electronics. Nuclear engineering, vehicle routing, optimization
transportation, Electric vehicles, vehicle to grid, greenhouse gas emissions, Electrical and Computer Engineering, 333, TK1-9971, Engineering, electrification, Electrical engineering. Electronics. Nuclear engineering, vehicle routing, optimization
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).15 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
