Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

Matthew J Eagon, Setayesh Fakhimi, George Lyu, Audrey Yang, Brian Lin, William F. Northrop

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

Original languageEnglish (US)
Title of host publication2022 IEEE Intelligent Vehicles Symposium, IV 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665488211
StatePublished - 2022
Event2022 IEEE Intelligent Vehicles Symposium, IV 2022 - Aachen, Germany
Duration: Jun 5 2022Jun 9 2022

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings


Conference2022 IEEE Intelligent Vehicles Symposium, IV 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


  • Smart infrastructure
  • charger placement
  • connected vehicles
  • electric vehicles


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