Optimal placement of electric vehicle chargers: A mixed-integer linear programming model

Joubin Zahiri Khameneh; Emmanuel Fagbenle; Joubin Zahiri Khameneh; Emmanuel Fagbenle

doi:10.3934/ctr.2026001

Clean Technologies and Recycling

2026, Volume 6, Issue 1: 1-32. doi: 10.3934/ctr.2026001

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Research article Topical Sections

Optimal placement of electric vehicle chargers: A mixed-integer linear programming model

Peter T. Paul College of Business and Economics, University of New Hampshire, Durham, NH, USA

Received: 28 July 2025 Revised: 21 November 2025 Accepted: 22 December 2025 Published: 06 January 2026

Electric vehicle adoption is growing, but New Hampshire lags in public charging infrastructure, especially in rural areas. This gap increases range anxiety and economic inefficiencies. In this study, we developed a mixed-integer linear programming (MILP) model to optimally locate new electric vehicle chargers statewide, maximizing coverage and equity under budget constraints. The model includes geographic coverage requirements, population-weighted equity, capacity limits, and a $\$$28 million budget. Moreover, the model recommends 855 Level 2 chargers and 149 Direct Current Fast Chargers (DCFCs) across 247 ZIP Codes, nearly doubling public charging capacity and achieving 98.8% coverage within defined service radii. The plan offers a cost-effective strategy that balances urban and rural needs. By integrating coverage, equity, and cost considerations, the model provides an adaptable framework for electric vehicle infrastructure planning and demonstrates how operations research supports sustainable transportation policy.
- Electric vehicles,
- charging infrastructure,
- mixed-integer linear programming,
- optimization,
- equity,
- transportation planning
Citation: Joubin Zahiri Khameneh, Emmanuel Fagbenle. Optimal placement of electric vehicle chargers: A mixed-integer linear programming model[J]. Clean Technologies and Recycling, 2026, 6(1): 1-32. doi: 10.3934/ctr.2026001

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Abstract

Electric vehicle adoption is growing, but New Hampshire lags in public charging infrastructure, especially in rural areas. This gap increases range anxiety and economic inefficiencies. In this study, we developed a mixed-integer linear programming (MILP) model to optimally locate new electric vehicle chargers statewide, maximizing coverage and equity under budget constraints. The model includes geographic coverage requirements, population-weighted equity, capacity limits, and a $\$$28 million budget. Moreover, the model recommends 855 Level 2 chargers and 149 Direct Current Fast Chargers (DCFCs) across 247 ZIP Codes, nearly doubling public charging capacity and achieving 98.8% coverage within defined service radii. The plan offers a cost-effective strategy that balances urban and rural needs. By integrating coverage, equity, and cost considerations, the model provides an adaptable framework for electric vehicle infrastructure planning and demonstrates how operations research supports sustainable transportation policy.

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