Support Urban Mobility and Electric Vehicle Charging: AI and Optimization Approach to Electric Vehicle Charging Infrastructure Planning and Charging Management

Primary supervisor

Hao Wang

The rapid growth of electric vehicles (EVs) is transforming the transportation systems worldwide. Both EV fleets and private EVs are emerging as a cleaner and more sustainable component of urban mobility, forming an effective way to solve environmental problems and reduce commute costs in future smart cities. Due to the complex spatiotemporal behaviors of passengers and their travel patterns, the unmanaged electric charging demand from EVs may significantly impact the existing transportation and electrical power infrastructure. Without strategic planning and effective operation, the accelerating EV usage could lead to significant challenges to traffic congestion, grid reliability, and suboptimal utilization of resources. Therefore, it is essential to develop reliable, robust, and resilient EV charging networks and charging strategies to address the above-mentioned challenges and facilitate smooth transition to a sustainable urban future.

    #sustainability

    Student cohort

    Double Semester

    Aim/outline

    This research aims to take pivotal steps to enhance the understanding of urban mobility and develop reliable, robust, and resilient EV charging networks and charging strategies.

    • We aim to analyze the real-world mobility patterns and charging demands of autonomous EVs, including the temporal and spatial distribution of trips, the variation in charging needs, and the impact of these factors on both transportation networks and power grids.
    • We will develop effective strategies for EV charging and routing that capitalize on the diversity of trip patterns and the availability of charging infrastructure. The approach will account for heterogeneous EV behaviors and preferences and will operate effectively in a dynamic and uncertain energy-transport environment.
    • By integrating insights from our behavioral analysis, we will provide data-driven recommendations for the planning and expansion of EV charging infrastructure. This will include identifying optimal locations for new charging stations, assessing the required capacity to meet future demands, and ensuring the resilience and efficiency of the charging network.
    • In addition, this research will explore broader implications such as equity in access to charging infrastructure, global experience and practice, and the role of policy in supporting sustainable mobility. We will consider the socio-economic factors that influence EV adoption and infrastructure usage, ensuring that our solutions contribute to a more inclusive and sustainable urban future.

    URLs/references

    This thesis project is aligned with the PhD project at https://www.monash.edu/data-futures-institute/study/phd-scholarship/mobility-data-driven-planning-of-electric-vehicle-charging-infrastructure-for-decarbonising-energy-and-transport-systems

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

    6) 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.

    7) J. Li^, A Chew*, Hao Wang, Investigating State-of-the-Art Planning Strategies for Electric Vehicle Charging Infrastructures in Coupled Transport and Power Networks: A Comprehensive Review, Progress in Energy, 2024.

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

    Required knowledge

    Python programming (required); Data analytics and deep learning (preferred); optimization/operations research (preferred).