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Autonomous electric vehicle fleets: data-driven analysis of mobility and impact on smart city infrastructure

Primary supervisor

Hao Wang

Autonomous electric vehicle (EV) fleets are emerging as an effective way to solve environmental problems and reduce commute costs in smart cities. Due to the complex spatiotemporal behaviors of passengers and their trips, the unmanaged electric charging demand from EV fleets will significantly impact the existing transportation and electric power infrastructure. Reliable charging networks and charging strategies for EV fleets are the prerequisites to the successful adoption of autonomous EV fleets.

We aim to take the first step to

  • analyze the real-world behaviors of EV fleets, including their mobility patterns and charging demand;
  • design AI-empowered strategies for EV charging and routing to leverage the diversity in the trip patterns and the availability of charging capabilities. The heterogenous EV behaviours and preferences under a dynamical energy-transport environment with uncertainties will be considered.
  • provide insights into charging infrastructure planning.


Student cohort

Double Semester


This project aims to leverage data analytics and machine learning tools to study the real-world mobility patterns and charging demand of EV fleets and to further predict/manage EV travels with smart charging.

  1. You will be provided with realistic trip record data;
  2. This project will reveal the mobility patterns including arrival and departure on a daily basis in a zonal system;
  3. This project will develop methods to estimate the charging demand of EV fleets in each zone/region based on their mobility patterns and manage the EV travels using deep learning.
  4. This project will develop AI solutions for EV charging management.


This thesis project is aligned with the PhD project at

Recent papers from the team:

1) J. Li, Yu Hui Yuan*, Q. Cui, Hao Wang, Cross-Entropy-Based Approach to Multi-Objective Electric Vehicle Charging Infrastructure Planning, 2023 IEEE IAS Industrial and Commercial Power System Asia Conference (IEEE I&CPS Asia), 2023.

2) Aditya Khele*, C. Jiang, Hao Wang, Fairness-Aware Optimization of Vehicle-to-Vehicle Interaction for Smart EV Charging Coordination, The 59th annual IEEE Industrial and Commercial Power System Technical Conference (IEEE I&CPS), 2023.

3) C. Jiang^, A. Liebman, Hao Wang, Network-Aware Electric Vehicle Coordination for Vehicle-to-Anything Value Stacking Considering Uncertainties, The 59th annual IEEE Industrial and Commercial Power System Technical Conference (IEEE I&CPS), 2023.

4) J. Fan^, Hao Wang, A. Liebman, MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange, The 49th Annual Conference of the IEEE Industrial Electronics Society (IEEE IECON), 2023.

5) J. Fan^, Hao Wang, A. Liebman, Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network, 2024 IEEE Power & Energy Society General Meeting (IEEE PESGM), 2024.

6) F. Ke, Hao Wang, Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data, 2024 IEEE Power & Energy Society General Meeting (IEEE PESGM), 2024.

7) Y. Wang#, Hao Wang, Reza Razzaghi, Mahdi Jalili, A. Liebman, Multi-Objective Coordinated EV Charging Strategy in Distribution Networks Using An Improved Augmented Epsilon-Constrained Method, Applied Energy, 2024.

(*Thesis students. ^HDR students. #Research Fellow)

Required knowledge

Programming knowledge (required), Python/R; Data analytics and deep learning (preferred).