A Comprehensive Analysis of EV Charging Demand Prediction, Infrastructure Planning, and Power Network Resilience in the Era of Electric Mobility

Project Abstract

Electric vehicles (EVs) have emerged as an appealing transportation mode due to their exceptional energy efficiency and minimal emissions. Therefore, it is necessary to equip government agencies with adept strategies for integrating the increasing EV market share into their transportation and energy networks. This research project has three pivotal objectives aimed at effectively addressing the challenges of EV adoption: predicting the future EV charging demands, providing policy recommendations for EV charging infrastructure planning aligned with charging demands and the capacities of transportation and power networks, and integrating diverse charging options, including public charging stations, home chargers, and innovative overnight park-and-charge facilities. To accomplish these, the project will first employ daily travel patterns from travel survey data, probabilistic models capturing charging randomness, and EV market share from literature to precisely predict charging demand. Then, a two-level mixed integer linear programming will be utilized to optimize the deployment of charging infrastructure while minimizing the long-term investment cost and congestion in the transportation network, considering the power network capacity. The results of the optimization problem will be used to derive the policy recommendations. Finally, the integration of overnight park-and-charge, home chargers, and public charging facilities into the planning problem will determine their optimal locations, capacities, and associated costs. This project will have a multitude of impacts. The prediction of EV charging demand, efficient planning of charging infrastructure, and provision of various charging options can facilitate the adoption of EVs, thus expediting the transition towards sustainable transportation systems. The introduction of innovative overnight park-and-charge facilities can promote equitable access to EVs, addressing accessibility disparities. The optimization of charging infrastructure planning also has the potential to mitigate traffic congestion and improve power grid resilience. Infrastructure planners, such as the local department of transportation and utility companies, will consult all outcomes of this project when siting and sizing new charging stations and innovative overnight park-and-charge facilities.

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Universities Involved

University of Maryland, College Park

Principle Investigators

Dr. Xianfeng Yang

Funding Sources and Amounts

USDOT: $100,000

Start Date

September 1, 2023

Completion Date

September 1, 2024

Expected Research Outcomes & Impacts

The outcomes of this project can be summarized in three main points: (1) a comprehensive prediction of future EV charging demand with spatial and temporal details, (2) policy recommendations for EV charging infrastructure planning derived from an optimization framework considering both transportation and power networks, and (3) a deployment plan outlining the estimated costs of a range of charging options, including public charging stations, home chargers, and innovative overnight park-and-charge facilities.

This project will have a multitude of broader impacts. Firstly, the study's insights into predicting EV charging demands and efficiently planning charging infrastructure can expedite the transition towards sustainable transportation systems. By strategically placing charging stations and accommodating various charging choices, the adoption of EVs can be facilitated, leading to reduced greenhouse gas emissions and decreased dependence on fossil fuels. Secondly, the introduction of innovative overnight park-and-charge facilities can promote equitable access to EVs. This can be particularly advantageous for individuals without home charging capabilities, fostering broader EV adoption and addressing accessibility disparities. Thirdly, the optimization of charging infrastructure planning, considering both transportation and power networks, has the potential to mitigate traffic congestion and prevent overloads in the power grid. Fourthly, the incorporation of advanced modeling techniques into the study can contribute to the advancement of predictive tools for EV charging demand and planning tools for EV charging infrastructure. Moreover, the proposed research and its constituent components hold significant relevance for a range of STEM fields, encompassing electrical engineering, transportation engineering, information science, operations research, decision science, and socio-economic planning sciences.

Subject Areas

Electric Vehicles, Infrastructure Design and Planning