Online Dynamic Pricing for Electric Vehicle Charging Stations With Reservations

This paper introduces a novel model for online dynamic pricing of electric vehicle charging services that integrates reservation, parking, and charging into a comprehensive bundle priced as a whole. Our approach focuses on the individual high-demand, fast-charging location, employing a Poisson process as a model of charging reservation arrivals, and develops an online dynamic pricing strategy optimized through a Markov Decision Process (MDP). A key contribution is the novel analysis of discretization error introduced when incorporating the continuous-time Poisson process into the discrete MDP framework. The MDP model’s feasibility is demonstrated with a heuristic dynamic pricing method based on Monte-Carlo tree search, offering a viable path for real-world applications.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2025-05-15
DOI
https://doi.org/10.1109/tits.2025.3565906
Citations
5
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
3.65

Funders

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article

Online Dynamic Pricing for Electric Vehicle Charging Stations With Reservations

Jiří Vokřínek, Jan Mrkos, Antonín Komenda, David Fiedler
5 citations
IEEE Transactions on Intelligent Transportation Systems
Electric Vehicles and Infrastructure
3.65
article

Online Dynamic Pricing for Electric Vehicle Charging Stations With Reservations

Jiří Vokřínek, Jan Mrkos, Antonín Komenda, David Fiedler
article en
5 citations

Abstract

This paper introduces a novel model for online dynamic pricing of electric vehicle charging services that integrates reservation, parking, and charging into a comprehensive bundle priced as a whole. Our approach focuses on the individual high-demand, fast-charging location, employing a Poisson process as a model of charging reservation arrivals, and develops an online dynamic pricing strategy optimized through a Markov Decision Process (MDP). A key contribution is the novel analysis of discretization error introduced when incorporating the continuous-time Poisson process into the discrete MDP framework. The MDP model’s feasibility is demonstrated with a heuristic dynamic pricing method based on Monte-Carlo tree search, offering a viable path for real-world applications.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(9)
Czech Technical University in Prague (CZ)
Grantová Agentura České Republiky
Openalex Percentile: Top 7%
Electric Vehicles and Infrastructure
3.65
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