Faculty/School

Faculty of Engineering

School of Electrical Engineering and Robotics

Topic status

We're looking for students to study this topic.

Research centre

Supervisors

Dr Maryam Haghighat
Position
Lecturer
Division / Faculty
Faculty of Engineering
Dr Yuchen Zhang
Position
Lecturer
Division / Faculty
Faculty of Engineering

Overview

Electric vehicles (EV) have played a major role in the sustainable energy transition through transport electrification. The increasingly higher EV penetration may substantially increase the electricity usage and peak demand in power distribution systems. The EV travelling and charging behaviours also bring uncertainties to the distribution network operation. Meeting these challenges, accurate EV charging demand forecast is imperatively needed to help capture and understand the future load demand variations in power systems. Based on their travel and charging needs, EV fleet serves as mobile energy carriers that link power distribution and transport networks. This project aims to investigate the interdependency of these two networks in terms of EV charging, and leverage the traffic flow data to develop a data-driven model for spatial-temporal EV charging demand forecast.

Research engagement

Literature review, data analytics based project

Research activities

  • Provide literature review on the state-of-the-art of EV charging demand forecast.
  • Work with urban traffic flow dataset, identify the data preprocessing needs and develop corresponding methodologies to ensure data integrity.
  • Implement data-driven techniques for EV charging demand forecast and performance validation.

The student will work with Dr Yuchen Zhang (expertise in power engineering) and Dr Maryam Haghighat (expertise in machine learning) to perform these activities.

Outcomes

This project aims to improve EV charging demand forecast performance with the aid of transport traffic flow data. The outcome from this project can potentially benefit both power and transport sectors, and contribute to a wide range of services across power systems and transport systems, for example, electrical load management, charging infrastructure planning, and travelling navigation.

Skills and experience

  • Strong programming background (preferably in Python).
  • Data analytics and machine learning experience preferred but not mandatory.
  • Knowledge in power engineering and power systems
  • Knowledge in electric vehicle and its charging characteristics is preferred

Start date

1 November, 2024

End date

28 February, 2025

Location

Garden Points campus

Keywords

Contact

Yuchen Zhang yuchen1.zhang@qut.edu.au