aceReinforcement Learning for Electric Vehicle Charging Management: Theory and Applications
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 va…
Reinforcement learning provides intelligent, scalable and adaptive control for electric vehicle charging station operations to address grid constraints, renewable integration and dynamic pricing.
Review identifies remaining methodological gaps and deployment challenges across scalability, uncertainty management, interpretability, and adaptability for EVCS control.
Evidence
- Peer-reviewedEnergies2025-10-01
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Truvace Impact Record TRV-2026-1054, v1: “Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications.” Truvace, 2026-09-10. /record/TRV-2026-1054 (accessed at citation time). sha256 d1ecfb29e145b6cc…
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