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Mobility data-driven planning of electric vehicle charging infrastructure for decarbonising energy and transport systems

Primary supervisor

Hao Wang


PhD project abstract

The adoption of electric vehicles (EVs) is widely accepted as a fundamental approach to mitigating climate change for a sustainable future with cleaner energy and transport systems. Furthermore, the technological advancements in batteries and chargers enable the massive deployment of EVs. However, the lack of planning for charging infrastructure and consumers’ concerns known as “range anxiety” are amongst the top barriers to the wide adoption of EVs. This project aims to develop a paradigm for data-driven modelling of EV behavours, optimisation for EV charging station planning, and AI-based pricing design for EV charging coordination. More specifically, this project has the following three research tasks.

  1. We will characterise realistic EV mobility behaviours and charging preferences using real-world spatio-temporal traffic data and open-access consumer surveys.
  2. We will develop an large-scale optimisation problem for determining the optimal placement and sizing of charging infrastructure.
  3. We will design an AI-empowered pricing scheme considering heterogenous EV behaviours and preferences under a dynamical energy-transport environment with uncertainties.

The outcomes of this project will provide new models and algorithms for energy-transport integration, advancing the knowledge of mitigation strategies for sustainable urban development.


PhD student role description

This project provides a perfect opportunity for the candidate to work in one of the most exciting problems of our time, i.e., mitigating climate change via transport electrification. In this project, the candidate will work with leading researchers in IT and engineering, access world-class research resources, learn state-of-the-art techniques, such as spatio-temporal data analytics, stochastic optimisation, multi-agent reinforcement learning, in an interdisciplinary area across energy and transport systems.

Role/contribution in the project

  • Collaborate with supervisors and other team members
  • Analyse the mobility data and design optimisation and machine learning algorithms
  • Validate the design on energy-transport simulation platforms
  • Present and promote research findings in international conferences and workshops
  • Publish scholarly works in top venues
  • Work with industry partners in energy and transport sectors if any.

Through this project, the candidate will become an expert in AI and data science for sustainable energy-transport interfacing system

  • with strong ability of problem-solving using AI and data science tools,
  • strong skills in analytical thinking and programming,
  • a deep understanding of critical infrastructure and sustainable development, building a unique blend of expertise and experiences for a future career in academia and/or AI, IT, Energy, and Transport industry.

Please don't apply through this website but submit the Expression of Interest via the Monash Data Futures Institute Application Link:

Required knowledge

  • Degree in Computer Science, IT, Civil/Transportation Engineering or equivalent
  • Strong programming skills in Python, Julia, etc.
  • Strong mathematical and analytical skills
  • Knowledge of electricity network analysis and simulation with network design knowledge highly desirable
  • Excellent written and verbal communication skills.

Project funding


Learn more about minimum entry requirements.