Towards Experiments for Comparing Anytime Algorithms to Optimize Assignments of Electric Vehicles To Charging Stations

Charging of electric vehicles (EVs) on highways must be efficient for ensuring e-mobility because of the comparably limited range of EVs and the potentially very long charging times during longer trips. To reduce the carbon footprint of EVs, they should be charged as much as possible with renewable...

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Vydáno v:Procedia computer science Ročník 263; s. 874 - 883
Hlavní autoři: Kammerhofer, Michael, Schwendinger, Benjamin, Hoch, Ralph, Sallinger, Christian, Kaindl, Hermann
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 2025
Témata:
Anytime algorithms
CO2 reduction
Electric vehicle charging
Experiments
Genetic algorithms
Multi-objective optimization
Anytime algorithms
Electric vehicle charging
Multi-objective optimization
CO2 reduction
Experiments
Genetic algorithms
ISSN:1877-0509, 1877-0509
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Shrnutí:Charging of electric vehicles (EVs) on highways must be efficient for ensuring e-mobility because of the comparably limited range of EVs and the potentially very long charging times during longer trips. To reduce the carbon footprint of EVs, they should be charged as much as possible with renewable energy, ideally when it is currently available. Hence, we use multi-objective optimization for the allocation of EVs to charging sites. Since the traffic situation is subject to rapid change, this needs to be dynamic ‘anytime’ optimization. In this paper, we address how to ensure that an algorithm used is really better in some aspect than another one. We propose controlled experiments in a simulation environment, where the resulting data are statistically tested to avoid believing into results that are due to random fuctuation. We both propose an experiment design and show how it was implemented for a specific comparison of such algorithms. In particular, we compare a genetic algorithm previously published with other algorithms and show its efficiency.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2025.07.105