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Agent-based simulation for Kansas EV infrastructure resilience
Heuer, Adelyn
Heuer, Adelyn
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Heuer_2025.pdf
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2025-04-11
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Heuer, A. 2025. Agent-based simulation for Kansas EV infrastructure resilience. -- In Proceedings: 21st Annual Symposium on Graduate Research and Scholarly Projects. Wichita, KS: Wichita State University
Abstract
INTRODUCTION: Electric vehicles are projected to comprise over 50 percent of vehicles on the road by the year 2050. However, the state of Kansas does not currently have the infrastructure (such as EV charging stations) to support this rapid increase. Defining optimal quantity and placement of high-powered charging stations ensures successful and safe statewide EV transportation at these rates. Additionally, EV infrastructure must prove to be resilient against extreme scenarios such as natural disasters that result in high charging demand due to mass evacuations.
PURPOSE: The goal of the study is to assess the resilience of current charging infrastructure along Kansas Interstate 70 against both typical and extreme scenarios and develop performance metrics such as charging station utilization, user range anxiety, Tesla versus non-Tesla performance, and expected impact of station shutdown or road closures.
METHODS: Research is conducted by developing an agent-based simulation using the modeling platform NetLogo. EV trips are generated from a Least Squares Optimization using traffic flow data in the state of Kansas. The simulation uses a road path mimicking major Kansas highways and interstates. It establishes a network of cities and “DC Fast” charging stations representing potential routes and stations available for statewide transportation purposes. By assigning agent or demographic-related behaviors to drivers as well as assigning variables (such as utilization rate or downtime due to maintenance) to respective charging stations, EV transportation is simulated across the state including necessary or preferred stops for charging.
RESULTS: This model allows the collection of various resilience performance metrics such as charging station utilization, waiting time, and charging queue length in response to the modification of input parameters including availability of specific charging stations, influx in traffic flow, or agent behaviors.
CONCLUSION: This model allows the identification of any deficiencies in the current EV infrastructure and can provide supportive analysis of solutions such as mobile charging units or information campaigns.
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Presented to the 21st Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 11, 2025.
Research completed in the Department of Industrial, Systems, and Manufacturing Engineering, College of Engineering.
Research completed in the Department of Industrial, Systems, and Manufacturing Engineering, College of Engineering.
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Wichita State University
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GRASP
v. 21
v. 21