A quantum optimization algorithm for optimal electric vehicle charging station placement for intercity trips

Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure, particularly to support long-distance travel. Identifying optimal charging station locations in large transportation networks presents an NP-hard combinatorial optimization problem, as the search space grows exponentially with the number of potential charging station locations. This thesis introduces a quantum search-based optimization algorithm designed to enhance the efficiency of solving this NP-hard problem for transportation networks. By leveraging quantum parallelism, amplitude amplification, and quantum phase estimation as a subroutine, the optimal solution is identified with a quadratic improvement in complexity compared to classical exact methods, such as branch and bound. The detailed design of a quantum circuit for optimizing the charging station locations is presented, and the complexity of the proposed quantum algorithm is analyzed.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Radvand, Tina

Contributors dc:contributor

• Talebpour, Alireza

• Electric Vehicle
• Charging Station Location
• Grover's Adaptive Search
• Quantum Optimization

Statement dc:rights

• Copyright © 2025 Tina Radvand

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129329

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129329