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This research aims to enhance electric vehicle charging management by integrating safety and cost-efficiency using reinforcement learning.

June 28, 2026Energy ReportsOpen Access

Safe and tariff-aware reinforcement learning for EV charging using real OCPP data

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This research aims to enhance electric vehicle charging management by integrating safety and cost-efficiency using reinforcement learning.
Developed a constrained multi-objective reinforcement learning framework (CMOR-PPO) for EV charging.
Employed a Lagrangian-based constraint update to enforce operational limits and ensure safety during charging.
Evaluated the approach using real-world Open Charge Point Protocol (OCPP) data under various demand scenarios.
Achieved a peak demand reduction of approximately 5% compared to uncontrolled strategies.
Operational costs were lowered while maintaining 100% charging completion with near-zero constraint violations.
Displayed stable performance under varying noise levels and demand conditions, affirming suitability for real-world applications.

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Randomized trial demonstrates reduced peak demand and operational costs in EV charging, suggesting efficient grid management solutions.

Hossen et al. (2026) studied this question.

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