What question did this study set out to answer?

The research aims to develop a reinforcement learning framework for managing electric vehicle integration into the power grid while preserving battery longevity.

August 13, 2026Iconic Research and Engineering Journals0 citationsOpen Access

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

VSVansh SharmaSRM Institute of Science and Technology HSH SarkarSRM Institute of Science and Technology LMLopamudra MazumderSRM Institute of Science and Technology

Key Points

The research aims to develop a reinforcement learning framework for managing electric vehicle integration into the power grid while preserving battery longevity.
Developed a reinforcement learning framework using proximal policy optimization (PPO)
Utilized the Indian Grid Master dataset to manage bidirectional vehicle-to-grid (V2G) power flow
Implemented an asymmetric reward function to prioritize battery preservation
Achieved mobility targets while optimizing for grid stability across various 10-hour shift profiles
Established a 90% state of charge (SoC) mobility requirement to balance energy outputs
Demonstrated effective carbon reduction and economic arbitrage through intelligent power management

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.

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