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Locating and sizing electric vehicle charging stations through multi-stage stochastic optimisation

Funding
Funded
Study mode
Full-time
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Start date
Year round
Subject area
Engineering
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Overview

Applications are invited for a fully funded joint PhD program between the University of Liverpool (UoL), UK and the National Tsing-Hua University (NTHU), Taiwan. The program focuses on locating and sizing electric vehicle (EV) charging stations using multi-stage stochastic optimization. The primary objective is to develop an optimization tool integrated with a spatial model to identify optimal locations for EV charging stations within a region.

About this opportunity

NTHU-UoL Dual PhD Programme between National Tsing Hua University in Taiwan and the University of Liverpool in the UK is a well-established programme, where students spending 2 years at both institutions. Working with world leading academics and research capabilities the PhD candidates will spend two years in each institution. Upon successful defence of their research work, the candidates will obtain dual PhD degrees.

As part of the global shift towards sustainability, the transportation sector is increasingly adopting electric vehicles (EVs) over traditional vehicles. Despite the growing adoption of EVs, driven by governmental incentives, there is a pressing need to establish sufficient charging infrastructure.

Optimal placement of charging stations is essential, considering factors such as demand, social equity, and integration with transportation networks while discouraging overreliance on private transport. Establishing a robust, equitable, and scalable charging infrastructure is recognised as crucial by many countries. Key considerations in the rollout of charging infrastructure include energy system integration, grid benefits, and minimising pavement clutter. While previous research has explored charging infrastructure from spatial and mathematical optimisation perspectives, a comprehensive approach that considers spatial, energy, and sociodemographic factors simultaneously is lacking.

This project aims to address this gap by developing a tool to assist planners and policymakers in locating EV charging stations based on specified criteria and available data. The research objectives include identifying critical parameters influencing charging station placement, formulating a multi-stage optimisation problem to maximise utilisation and ensure equitable distribution, and creating an interactive tool for stakeholders to visualise optimal charging station locations based on regional needs and constraints. This tool aims to empower local stakeholders to strategically deploy charging infrastructure tailored to their specific contexts, informing local planning and policymaking for the optimal establishment of EV charging infrastructure.

The successful PhD candidate will spend the first two years at UoL, followed by two years at NTHU. At UoL, the student will join the Department of Civil and Environmental Engineering within the School of Engineering, where they will formulate their research problem and conduct an intensive literature review in the field of EV charging placement studies and operations research methodologies. They will familiarise themselves with the transport network plan and model for the UK and Taiwan, as well as sociodemographic data available at a spatial scale. Additionally, the student will self-train in GIS modelling tools and macroscopic transport modelling tools with guidance from the UoL lead supervisor and is encouraged to take transport-related modules offered at UoL.

Training will be provided to initiate these investigations. At NTHU, the student will join the Department of Industrial Engineering and Engineering Management, undertaking courses in Integer Programming and Network Analysis, Stochastic Optimisation, Nonlinear Programming, and Operations Research. The student will be closely supervised by both lead supervisors throughout the program.

Who is this opportunity for?

This cross-disciplinary project is suitable for candidates with backgrounds in Transportation Engineering, Civil Engineering, Industrial Engineering, Operations Research, or related disciplines.

Candidates will have, or be due to obtain, a master’s degree or equivalent from a reputable University in an appropriate field of Engineering. Exceptional candidates with a First Class bachelor’s degree in an appropriate field will also be considered.

The ideal candidate should have an interest in computational methods and applying these techniques to address complex problems in advanced technologies. Coding skills are highly desirable.

We want all of our staff and students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances.

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How to apply

  1. 1. Contact supervisors

  2. 2. Prepare your application documents

    • Degree certificates to date
    • Academic transcripts
    • An up-to-date CV
    • A cover letter/personal statement
    • Two academic references.
  3. 3. Apply

    Finally, register and apply online. You'll receive an email acknowledgment once you've submitted your application. We'll be in touch with further details about what happens next.

    You should complete and submit an application for Civil Engineering PhD. The application deadline is 31 May 2025.

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Funding your PhD

This funded studentship will cover tuition fees and pay a maintenance grant similar to a UKRI studentship (£18,622/year) for 2 years while in Liverpool and 15233 NDT/month for 2 years while in Taiwan. The studentship also comes with additional financial support of a research training support grant which will fund the cost of materials, conference attendance etc.

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Contact us

Have a question about this research opportunity or studying a PhD with us? Please get in touch with us, using the contact details below, and we’ll be happy to assist you.

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