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Scheduling Electric-Vehicle Charging Under Grid Capacity and Minimum Charge Rates

Authors Bruna Cavalcanti Lauro , Matúš Mihalák , Filip Schlembach , Evgueni Smirnov

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OASIcs.ATMOS.2026.9.pdf
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Document Identifiers

Subject Classification

ACM Subject Classification
  • Applied computing → Operations research
  • Computing methodologies → Machine learning
Keywords
  • Electric-Vehicle Charging
  • Scheduling
  • Grid-Capacity Constraints
  • Charge-Rate Lower-Bounds

Metrics

Abstract

Electric vehicle adoption is growing rapidly, and parking facilities face increasing pressure to expand their charging infrastructure. This paper studies the power allocation scheduling problem that arises when a parking facility with fixed grid-capacity limit must distribute power to connected electric vehicles, under given lower and upper bounds on the charge rate at any given time. We focus on the optimization goal of maximizing the number of vehicles that get charged a given target of power, with the secondary goal of providing as much power as possible. We show that the problem is NP-hard. We formulate the problem as a mixed-integer linear program (MILP) that establishes a theoretical upper bound on performance of any online algorithm, which is the main goal of the real-world problem. We design and compare a few online scheduling strategies against the offline bound, using a real-world instance of vehicles and testing across 6 to 40 chargers. We use MILP as an online strategy, classical earliest-deadline first greedy algorithm, and we also design data-driven approaches using machine learning techniques. Results show that the simplest static load balancing approach cannot compete with the best performing online approach, and that the real-world problem can benefit from dynamic load balancing strategies such as those designed and evaluated in this paper.

Cite As Get BibTex

Bruna Cavalcanti Lauro, Matúš Mihalák, Filip Schlembach, and Evgueni Smirnov. Scheduling Electric-Vehicle Charging Under Grid Capacity and Minimum Charge Rates. In 26th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2026). Open Access Series in Informatics (OASIcs), Volume 147, pp. 9:1-9:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.ATMOS.2026.9

Author Details

Bruna Cavalcanti Lauro
  • Department of Advanced Computing Sciences, Maastricht University, The Netherlands
Matúš Mihalák
  • Department of Advanced Computing Sciences, Maastricht University, The Netherlands
Filip Schlembach
  • Department of Advanced Computing Sciences, Maastricht University, The Netherlands
Evgueni Smirnov
  • Department of Advanced Computing Sciences, Maastricht University, The Netherlands

Acknowledgements

We thank the anonymous reviewers for very thorough and inspiring comments, which helped to improve the paper.

References

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