The global adoption of electric vehicles (EV) expanded notably over the last decade, created opportunities for grid integration through flexible charging and vehicle to grid. This project aims to develop solutions that will help EV Owners and Fleet Managers with smart charging/discharging decisions and aggregation of EV resources to support the grid.
The flexibility offered by the grid-tied EVs will allow electricity providers to harness the massive storage capacity through opportunities such as flexible smart charging, virtual power plants, and vehicle to grid or vehicle to premises. However, these impose many challenges to the EV owners and grid managers.
This project aims to develop strategies that will help EV owners and fleet managers with smart charging decisions and aggregation of EV resources to support the grid, through optimised smart charging strategies to minimise operational costs of EVs, and to help shave peak load, address minimum load risks, control grid active and reactive power, and optimise renewable energy use.
Expected outputs of this project include:
- Design and development of efficient charging (and discharging) scheduling algorithms and software systems (including cloud and mobile application) to minimize the total operation cost of EVs and associated systems and battery degradation.
- Design and development of an aggregator software system that combines individual EVs information to collectively exploit EV’s potential to shave peak load, control grid active and reactive power, and support renewable energy sources for transmission system operators and distribution system operators.
- Building a simulation tool that simplifies the evaluation of vehicle to grid and smart charging strategies along with grid distribution for both electricity providers and EV owners.
- Design and development of business models, tariffs, and control solutions that respond to energy market signals and network constraints.
This PhD will be supported by an Industry Reference Group throughout the project.
The PhD is funded by a scholarship for 3 years and 3 months (with the option to apply for up to an additional 3 months) by the RACE for 2030 Collaborative Research Centre and Enzen is the Industry Partner on the project. This PhD will be completed through a PhD by publication/compilation or thesis with a minimum of three peer-reviewed publications and will commence with an industry-focused Rapid Review. You can find out more about RACE here.
RACE for 2030 will provide three years of funding at $38,000 per annum (tax exempt) via a student scholarship managed by Monash University. RACE will also supply up to $3,000 per annum for expenses for the candidate, for items such as a computer, publishing fees, travel, or conference costs.
- Scholarships are available for Australian residents and citizens. We encourage female-identifying or Indigenous applicants. Consideration of the RACE for 2030 scholarship may also be given to onshore international students, however, tuition fees still apply.
- Applicants should have a first-class Honours or Masters degree or equivalent in a related discipline, OR a combination of an upper second-class honour’s degree or equivalent in a related discipline together with a minimum of five years equivalent full-time professional work experience in a relevant field.
- Applicants must be eligible for enrolment in their chosen course at Monash University. It is recommended that students obtain relevant postgraduate information from Monash University before pursuing a scholarship inquiry.
- Applicants must be studying full-time.
For further information please contact:
Name: Adel N. Toosi
Organisation/Department: Monash University / Software Systems and Cybersecurity
Application closing date
Skills and experience
In addition to the eligibility criteria, candidates should also have the following skills and/or experience:
- Bachelor with Honours or Masters by research degree in Computer Science, or relevant fields with GPA 80%+ from a reputed university
- Solid programming skills
- Some experience with, or interest in, electric vehicles and smart grid systems
- Strong algorithm design skills
- Knowledge of Distributed Systems
- Excellent communication skills
- Refereed publications including journal or conference of high repute in the relevant area
- Basic software engineering skills
- Background in Machine Learning, Optimisation, and Data Structures
During the selection process, candidates will also be assessed upon their ability to:
- Independently pursue their work
- Collaborate with others
- Have a professional approach
- Analyse and work with complex issues and
- Formulate scientific texts