Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration
A new study proposes an emission-aware reinforcement learning strategy for electric vehicle charging. This approach aims to reduce carbon emissions while managing the challenges posed by increased EV adoption and renewable energy variability. The proposed method significantly outperforms traditional strategies in terms of emission reduction and grid compliance.
- ▪The study introduces a reinforcement learning strategy based on the Soft Actor Critic algorithm to optimize electric vehicle charging.
- ▪The RL agent achieved a carbon intensity as low as 23.96 grams of CO2 per kWh under 50% wind penetration, representing an 87% reduction compared to uncontrolled charging.
- ▪The research compares nine control strategies across five renewable penetration scenarios, demonstrating the effectiveness of the proposed RL approach.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24543 (cs) [Submitted on 23 May 2026] Title:Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration Authors:Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon, Shafkat Khan Siam, Shafiuzzaman K Khadem, Krishnendu Guha, Mayeen U Khandaker, Md. Noor-A-Rahim View a PDF of the paper titled Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration, by Ninglin Ou and 7 other authors View PDF HTML (experimental) Abstract:The rapid growth of Electric Vehicle (EV) adoption challenges power distribution networks through peak load spikes, voltage instability, and…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
