A comparative study of multi-objective and neuroevolutionary-based reinforcement learning algorithms for optimizing electric vehicle charging and load management

Neele Kemper, Michael Heider, Dirk Pietruschka and Jörg Hähner

Applied Energy, 2025, vol. 391, issue C, No S0306261925006208

Abstract: The electrification of transportation requires the development of smart charging management systems for electric vehicles to optimize grid performance and enhance user satisfaction. However, existing methods often reduce multi-objective problems to single-objective formulations, limiting their ability to balance conflicting objectives and requiring iterative runs for diverse solutions.

Keywords: Charging management; Electric vehicle charging; Evolutionary reinforcement learning; Multi-objective reinforcement learning; Neuroevolution; Smart grid (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125890

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