计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 241200015-5.doi: 10.11896/jsjkx.241200015

• 人工智能 • 上一篇    下一篇

Q学习驱动的联盟链框架下电动汽车充放电优化调度算法

曹永胜   

  1. 上海交通大学人工智能研究院 上海 200240
    上海电机学院电子信息学院 上海 201306
  • 出版日期:2025-11-15 发布日期:2025-11-10
  • 通讯作者: 曹永胜(yongshengcao@sjtu.edu.cn)
  • 基金资助:
    上海市“科技创新行动计划”启明星项目(扬帆专项)(22YF1411900);中国博士后基金特别资助项目(2022TQ0210);国家社会科学基金(20&ZD199);上海市司法鉴定协会课题(SHSFJD2024-013);国家社科基金重大项目(21&ZD200)

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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摘要： 随着电动汽车数量的增加,其对电力系统的影响日益显著。文中提出了一种基于Q学习与联盟链相结合的优化调度算法,旨在解决电动汽车充放电过程中的电力调度及交易支付安全问题。首先,利用联盟链构建了一个安全可靠的电动汽车电力交易平台,确保了交易数据的不可篡改性和可追溯性。接着,设计了一个考虑多种物理约束条件下的电动汽车充放电模型,包括电池老化、用户等待时间等关键因素。在此基础上,设计了一种基于Q学习的智能调度算法,以寻找最优的充放电策略,从而降低系统的综合成本并提高效率。通过仿真实验验证,该方法不仅能够有效保障交易的安全性,同时还能显著减少系统运营成本,证明了所提方案的有效性和实用性。

关键词: 网联电动汽车, 联盟链, Q学习, 电池老化, 电力调度

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

中图分类号: 

  • TP311
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