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  Modelling and optimisation of infrastructure for the decarbonisation of transport in rural areas


   School of Computing, Engineering & the Built Environment

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  Dr S Tingas, Dr N Urquhart, Dr F Sukki  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The decarbonisation of the transport sector (primarily through electrification) has become one of the priorities of most advanced economies nowadays, including the UK, in the fight against climate change. This transformation is usually supported by national legislation that aims to facilitate the transition to more environmentally friendly technologies. For instance, the UK has recently mandated the ban on all new petrol and diesel car sales by 2030. Similar initiatives were undertaken by other countries like the Sweden, Norway, Denmark, Austria, Japan, Italy and many others.

In 2021, 18.6% of the new car registrations in the UK were plug-in EVs (BEVs and PHEVs), a significant increase from 2020 when only 10.7% of the new car registrations were related to plug-in EVs. Yet, in 2022, the share of new plug-in sales has been 20.6%, a negligible increase compared to the previous year. Hydrogen fuel cell electric vehicles (FCEVs) are perceived as an alternative to BEVs and PHEVs, albeit their retail prices are still inaccessible to most buyers. Yet, it is expected that FCEVs will be an important part of the decarbonisation process and will eventually take a considerable part of the market share.

A necessary condition for the effective adoption of BEVs, PHEVs and FCEVs is the availability and accessibility to the required infrastructure, i.e., charging/refuelling stations. The UK EV infrastructure strategy has a provision that more than 300,000 charging points will be available by 2030. The available technologies for such stations currently can vary a lot hence the decision on the appropriate type is not straightforward. In addition to that, the exact location for the installation of a charging point is usually the result of an optimisation process where parameters such as the potential available users, safety, accessibility, land use, visibility, surrounding street network, traffic flow and many others are factored.

Optimisation approaches have been studied extensively in urban setups, however, there is limited understanding on the required approaches and appropriate technologies (e.g., types of charging stations, charging vs H2 refuelling stations, etc) to be used in rural setups, where the demands and the available solutions will be widely different. This becomes of paramount importance for countries like Scotland that are predominantly rural; with a population density 70 people per km2, 91% of Scotland's population lives in communities, which make up only 2.3% of Scotland's total land area.

The objective of this work will be to develop appropriate models for establishing networks of charging and/or refuelling stations appropriate for rural areas, using evolutionary and machine learning algorithms. These models will need consider among others, the requirements of the distribution network (centralised vs decentralised), the possible revenue, the available public transport, the convenience of the users, the renewable energy sources, the local geography and others.

Academic qualifications 

A first-class honours degree, or a distinction at master level, or equivalent achievements ideally in Electrical Engineering, Mechanical Engineering, or Civil Engineering. 

English language requirement 

If your first language is not English, comply with the University requirements for research degree programmes in terms of English language

Application process 

Prospective applicants are encouraged to contact the supervisor, Dr Stathis Tingas () to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

The application must include:  

Research project outline of 2 pages (list of references excluded). The outline may provide details about 

  • Background and motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results. 
  • Research questions or 
  • Methodology: types of data to be used, approach to data collection, and data analysis methods. 
  • List of references 

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support. 

  • Statement no longer than 1 page describing your motivations and fit with the project. 
  • Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate. 
  • Supporting documents will have to be submitted by successful candidates. 
  • Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here

Applications can be submitted here.

Download a copy of the project details here

Biological Sciences (4) Mathematics (25)

References

[1] 1. Kchaou-Boujelben, M. (2021). Charging station location problem: A comprehensive review on models and solution approaches. Transportation Research Part C: Emerging Technologies, 132, 103376.
[2] 2. Gupta, R. S., Tyagi, A., & Anand, S. (2021). Optimal allocation of electric vehicles charging infrastructure, policies and future trends. Journal of Energy Storage, 43, 103291.
[3] 3. Metais, M. O., Jouini, O., Perez, Y., Berrada, J., & Suomalainen, E. (2022). Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options. Renewable and Sustainable Energy Reviews, 153, 111719.
[4] 4. Ahmad, F., Iqbal, A., Ashraf, I., & Marzband, M. (2022). Optimal location of electric vehicle charging station and its impact on distribution network: A review. Energy Reports, 8, 2314-2333

 About the Project