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Related Experiment Video

Updated: Jan 8, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem.

Kazi Ashik Islam1, Aparna Kishore1, Rounak Meyur2

  • 1Department of Computer Science, Biocomplexity Institute, University of Virginia, Charlottesville, VA 22903.

Proceedings of the National Academy of Sciences of the United States of America
|December 15, 2025
PubMed
Summary

Charge-map, a new framework, optimizes electric vehicle (EV) charging infrastructure by simulating demand and planning station placement. This data-driven approach significantly reduces charging detours and wait times, supporting widespread EV adoption.

Keywords:
charging infrastructureelectric vehiclelocation optimizationmobility simulationpower grid

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Area of Science:

  • Urban Planning and Transportation Science
  • Electrical Engineering and Power Systems
  • Computational Science and Simulation

Background:

  • The rapid growth of electric vehicles (EVs) necessitates robust charging infrastructure.
  • Expanding EV charging infrastructure is complex, involving demand, grid capacity, and cost factors.

Purpose of the Study:

  • To present charge-map, a data-driven simulation-optimization framework for effective EV charging infrastructure planning.
  • To ensure a positive charging experience for individual EV owners.

Main Methods:

  • Developed charge-map, integrating agent-based simulation for demand prediction and optimization for station placement.
  • Modeled EV adopter mobility and charging behavior to estimate spatiotemporal demand.
  • Optimized new station/charger locations and capacities to minimize detour distances and wait times within budget constraints.
  • Assessed power grid impact and transformer capacity requirements.

Main Results:

  • charge-map can support ~198,600 EVs with 1,305 new stations and 2,164 chargers in Virginia.
  • Reduced average detour distances by 66% and wait times by 72%.
  • Identified minimal transformer upgrades needed (1.8% residential, >80% commercial supportable with 25-50 kVA).

Conclusions:

  • charge-map offers data-driven insights for cost-effective EV charging infrastructure expansion.
  • The framework facilitates targeted investments for sustainable EV integration.
  • Provides crucial data for policymakers and urban planners in developing EV infrastructure.