Posted in | News | Electric Vehicles

Dynamic Computational Tool Enhances User Access to EV Charging

North Carolina State University scientists have devised a dynamic computational tool to aid in improving user access to electric vehicle (EV) charging stations, to make EVs more appealing to drivers.

Dynamic Computational Tool Enhances User Access to EV Charging

Image Credit: Rick Govic.

We already know that there is a need for EV charging networks that are flexible, in order to support the adoption of EVs. That’s because there is tremendous variability in when and where people want to charge their vehicles, how much time they can spend at a charging station, how long it takes to charge their vehicles, and so on.

Leila Hajibabai, Study Corresponding Author and Assistant Professor, Fitts Department of Industrial and Systems Engineering, North Carolina State University

Leila Hajibabai adds, “The fundamental question we wanted to address with this work is: What is the best way to manage existing charging station infrastructure in order to best meet the demands of electric vehicle users?

The researchers intended to address that issue from the user's standpoint; therefore, they concentrated on issues that are essential to EV drivers. How much time will it take to get to a charging station? How much does it cost to use the charging station?

They also concentrated on issues like how long will the driver have to wait for a charging station, and the penalties for lingering longer at a charging station.

The researchers devised a method for accounting for all of these elements in a complex computational model based on a game theory framework.

The technique accomplishes two goals. First, it assists users in locating the nearest charging station that satisfies their requirements. Second, it contains a dynamic system that charging station operators may utilize to determine how long vehicles can stay at a charging station before being replaced by the next vehicle.

These outcomes are themselves dynamic—they evolve as additional data comes in about how users are making use of charging facilities.

Leila Hajibabai, Study Corresponding Author and Assistant Professor, Fitts Department of Industrial and Systems Engineering, North Carolina State University

For instance, based on whether any spaces are available, a user's nearest available charging station may change. Furthermore, the length of time customers may spend at a charging station may vary from day to day to reflect how people utilize different charging facilities.

There’s no clear real-world benchmark that we can use to assess the extent to which our technique would improve user access to charging facilities. But in simulations, the technique did improve user access. The simulations also suggest that flexibility in when charging station slots are available was a key predictor of which stations users would visit.

Leila Hajibabai, Study Corresponding Author and Assistant Professor, Fitts Department of Industrial and Systems Engineering, North Carolina State University

Leila Hajibabai concludes, “A next step would be to work with existing charging station networks to pilot the technique and assess its performance in a real-world setting.”

Journal Reference:

Hajibabai, L., et al. (2022) A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities. IEEE Transactions on Intelligent Transportation Systems. doi.org/10.1109/TITS.2022.3212017

Source: https://www.ncsu.edu/

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Submit
Azthena logo

AZoM.com powered by Azthena AI

Your AI Assistant finding answers from trusted AZoM content

Azthena logo with the word Azthena

Your AI Powered Scientific Assistant

Hi, I'm Azthena, you can trust me to find commercial scientific answers from AZoNetwork.com.

A few things you need to know before we start. Please read and accept to continue.

  • Use of “Azthena” is subject to the terms and conditions of use as set out by OpenAI.
  • Content provided on any AZoNetwork sites are subject to the site Terms & Conditions and Privacy Policy.
  • Large Language Models can make mistakes. Consider checking important information.

Great. Ask your question.

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.