Study level

  • Honours

Faculty/School

Faculty of Science

School of Information Systems

Topic status

We're looking for students to study this topic.

Research centre

Supervisors

Professor Alistair Barros
Position
Head of School, Information Systems
Division / Faculty
Faculty of Science
Dr Babar Shahzaad
Position
Lecturer (TIEA)
Division / Faculty
Faculty of Science

Overview

The number of Electric Vehicles (EVs) on the road is expected to reach 145 million by 2030. As the number of EVs on the road increases, the demand for energy to charge these vehicles also grows. Traditional charging infrastructure may not meet the increasing energy demands, which may lead to increased waiting time in those charging stations. The EV-EV trading scheme is a promising solution that allows EV owners to access additional energy from nearby EVs. This scheme has attracted a lot of attention both from academia and industry as it addresses the growing energy supply-demand challenges and offers incentives to EV owners.

This research project aims to collect a dataset for real-world EV charging considering the availability of charging stations and congestion conditions. This dataset will record various attributes including location, time, and available energy over the time of the EVs. This dataset will be used for finding various trends such as energy consumption and supply and demand gap analysis at certain charging stations using machine learning techniques. Furthermore, this project will assess the potential for EVs trading and contribute to developing a sustainable trading system.

Research activities

  • Ice breaking with EV-EV trading system.
  • Listing a concrete set of attributes for dataset collection.
  • Collecting and augmenting datasets using existing real-world datasets.
  • Data analysis using state-of-the-art machine learning algorithms.
  • Maintaining the progress document to record the progress, challenges faced, and solutions.

Outcomes

The outcomes of this research will be the following:

  • an EV-EV trading dataset
  • a demonstration/conference paper.

Skills and experience

An ideal candidate should possess the following skills and experience:

  • experience with python programming and python libraries especially NumPy and Pandas
  • familiarity with GitHub and version control is advantageous
  • data visualization using Matplotlib
  • experience with state-of-the-art machine learning algorithms.

Keywords

Contact

Contact the supervisor for more information.