A static modeling framework for evaluating the flood resilience of real-world electric vehicle charging networks
Jagtap, Sumeet
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https://hdl.handle.net/2142/129630
Description
Title
A static modeling framework for evaluating the flood resilience of real-world electric vehicle charging networks
Author(s)
Jagtap, Sumeet
Issue Date
2025-05-09
Director of Research (if dissertation) or Advisor (if thesis)
Wang, Pingfeng
Department of Study
Industrial&Enterprise Sys Eng
Discipline
Industrial Engineering
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
electric vehicle infrastructure
flood resilience
charging networks
network modeling
static simulation
resilience metrics
graph modeling
Abstract
As electric vehicles (EVs) become increasingly central to modern transportation, the resilience of the supporting EV charging infrastructure has emerged as a critical concern, particularly in the face of climate-related disruptions such as flooding. This thesis presents a static, simulation-based framework to evaluate the flood resilience of real-world EV charging networks (EVCNs). Instead of generating synthetic or hypothetical networks, the study constructs a data-driven graph model using publicly available datasets, where EV charging stations serve as nodes and geographic proximity defines the edges.
Flood scenarios are simulated by removing nodes and their associated edges based on the geographic flood vulnerability of each station. Network resilience is then quantified using adapted metrics from the resilience framework proposed by Wu and Wang (2022) and others, including connectivity retention, redundancy, accessibility loss, and systemic degradation. A resilience curve is developed to capture the overall performance decline of the network across disruption levels, with the area under the curve serving as a proxy for systemic resilience. The selected case study of Miami-Dade County demonstrates how a real-world EVCN responds under increasing levels of disruption and reveals critical thresholds in its network performance.
By grounding the analysis in real-world empirical data and emphasizing static evaluation, this methodology offers a diagnostic tool for assessing the vulnerability of existing EVCNs. It provides a practical foundation for resilience benchmarking as well as comparative resilience studies across regions where EV infrastructure is emerging, and aims to support the design of more robust EV infrastructure systems in flood-prone regions.
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