Reinforcement Learning for Intelligent Vehicle-to-Grid Integration: Balancing Clean Energy Utilization and Battery Degradation Preservation

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@article{1722214,
      author = {Vansh Sharma, Hrishita Sarkar, Lopamudra Mazumder},
      title = {Reinforcement Learning for Intelligent Vehicle-to-Grid Integration: Balancing Clean Energy Utilization and Battery Degradation Preservation},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {853-859},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722214.pdf},
      abstract = {The transition toward sustainable mobility requires an intelligent integration of Electric Vehicles (EVs) into the power grid. This paper proposes a Reinforcement Learning (RL) framework using Proximal Policy Optimization (PPO) to manage bidirectional Vehicle-to-Grid (V2G) power flow. Utilizing the Indian Grid Master dataset, the system optimizes for economic arbitrage and carbon reduction while strictly adhering to a 90% State of Charge (SoC) mobility requirement. A core innovation of this work is an asymmetric reward function that applies a 35x penalty to battery discharge relative to charging, ensuring hardware longevity. Results across various 10-hour shift profiles demonstrate the agent's ability to achieve mobility targets while maximizing grid stability.},
      keywords = {V2G, Reinforcement Learning, PPO, Battery Degradation, Indian Grid Master, EV2Gym.},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722214}
  }