Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 241200015-5.doi: 10.11896/jsjkx.241200015

• Artificial Intelligence • Previous Articles     Next Articles

Optimal Scheduling Algorithm for Electric Vehicle Charging and Discharging in Q-Learning Based Consortium Blockchain Framework

CAO Yongsheng   

  1. Artificial Intelligence Research Institute,Shanghai Jiao Tong University,Shanghai 200240,China
    School of Electronic Information Engineering,Shanghai Dianji University,Shanghai 201306,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    Shanghai Science and Technology Innovation Action Plan Morning Star Project (Sail Special)(22YF1411900),China Postdoctoral Science Foundation(2022TQ0210),National Social Science Foundation of China(20&ZD199),Research Project of the Shanghai Judicial Identification Association(SHSFJD2024-013) and National Social Science Foundation of China(21&ZD200).

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Abstract: The increasing number of grid-connected electric vehicles poses new challenges to the power system,particularly in terms of efficient energy management and secure trading.This paper introduces an optimization scheduling algorithm that integrates Q-learning with a consortium blockchain framework for EV charging and discharging.Initially,a secure and reliable power trading platform is established using consortium blockchain technology,ensuring the immutability and traceability of transactions.A comprehensive EV charging and discharging model is then developed,taking into account various physical constraints such as battery degradation and user waiting time.Based on this,a Q-learning-based intelligent scheduling algorithm is designed to identify optimal charging and discharging strategies,aiming at minimizing the overall system cost while enhancing operational efficiency.Simulation results demonstrate that the proposed method not only ensures the security of transactions but also significantly reduces system costs,validating its effectiveness and practicality.

Key words: Grid-connected electric vehicle, Consortium blockchain, Q learning, Battery degradation, Power scheduling

CLC Number: 

  • TP311
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