Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications
Panagiotis Michailidis (),
Iakovos Michailidis and
Elias Kosmatopoulos
Additional contact information
Panagiotis Michailidis: Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
Iakovos Michailidis: Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
Elias Kosmatopoulos: Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
Energies, 2025, vol. 18, issue 19, 1-50
Abstract:
The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across various charging contexts and control scenarios. This paper categorizes RL methodologies from value-based to actor–critic and hybrid frameworks, and explores their integration with optimization techniques, forecasting models, and multi-agent coordination strategies. By examining key design aspects—including agent structures, training schemes, coordination mechanisms, reward formulation, data usage, and evaluation protocols—this review identifies broader trends across central control dimensions such as scalability, uncertainty management, interpretability, and adaptability. In addition, the review assesses common baselines, performance metrics, and validation settings used in the literature, linking algorithmic developments with real-world deployment needs. By bridging theoretical principles with practical insights, this work provides comprehensive directions for future RL applications in EVCS control, while identifying methodological gaps and opportunities for safer, more efficient, and sustainable operation.
Keywords: reinforcement learning (RL); electric vehicles (EVs); electric vehicle charging stations (EVCSs); charging management; battery systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/1996-1073/18/19/5225/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/19/5225/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:19:p:5225-:d:1762919
Access Statistics for this article
Energies is currently edited by Ms. Cassie Shen
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().