• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2025, Vol. 61 ›› Issue (12): 217-225.doi: 10.3901/JME.2025.12.217

• 运载工程 • 上一篇    

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面向削峰填谷的住宅区电动汽车变功率实时充电调控策略

黎小慧1,2,3, 黄志嘉1,2,3, 张雷1,2,3, 王震坡1,2,3   

  1. 1. 北京理工大学机械与车辆学院 北京 100081;
    2. 电动车辆国家工程研究中心 北京 100081;
    3. 北京电动车辆协同创新中心 北京 100081
  • 收稿日期:2024-08-19 修回日期:2025-02-21 发布日期:2025-08-07
  • 作者简介:黎小慧,女,1994年出生,博士研究生。主要研究方向为新能源汽车与电网智慧互动。E-mail:xiaohui_li95@bit.edu.cn;黄志嘉,男,1997年出生,博士研究生。主要研究方向为新能源汽车充电需求预测与基础设施规划。E-mail:1906243767@qq.com;张雷(通信作者),男,1987年出生,博士,副教授,博士研究生导师。主要研究方向为电动车辆储能系统管理技术,智能网联新能源汽车决策、规划与主动安全控制技术。E-mail:lei_zhang@bit.edu.cn
  • 基金资助:
    国家重点研发计划资助项目(2022YFE0103000)。

Variable-rate-charging-based Real-time Charging Control Strategy for Residential Electric Vehicles for Load Flattening

LI Xiaohui1,2,3, HUANG Zhijia1,2,3, ZHANG Lei1,2,3, WANG Zhenpo1,2,3   

  1. 1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081;
    2. National Engineering Research Center for Electric Vehicles, Beijing, 100081;
    3. Collaborative Innovation Center for Electric Vehicles in Beijing, Beijing, 100081
  • Received:2024-08-19 Revised:2025-02-21 Published:2025-08-07

摘要: 面向充电系统实时交互活跃的住宅区电动汽车充电调控场景,以用户为中心、以负荷削峰填谷为目标,提出一种快速求解的电动汽车变功率充电调控方法,并通过北京某车网互动试点试验验证了方法的有效性。根据住宅区历史基础电力负荷情况,利用近似负荷方差最小化目标函数快速定义电动汽车聚合充电目标曲线,进而根据用户充电灵活性动态制定每辆到达车辆的变功率充电计划。在满足用户充电需求的前提下,该策略调节车辆充电功率以最大限度地跟踪聚合负荷目标,从而减小整体电力峰值负荷与方差。结果表明,该策略在线计算效率高,生成单辆车优化充电计划时间为14~16 ms,支持与用户快速实时交互,从而吸引更多用户参与充电调控;相较于满功率自由充电,变功率充电调控充电结束时间平均推迟3.33 h,使住宅区整体电力负荷方差有效降低了11%。

关键词: 电动汽车, 充电策略, 削峰填谷, 充电负荷, 用户交互

Abstract: A fast-solvable, user-centered, real-time electric vehicle(EV) charging control strategy using variable-rate charging approach has been proposed, aimed at peak load shaving and valley filling in residential scenarios where real-time interactions with the charging system are frequent. The load-flattening efficacy is validated through a pilot experiment in Beijing. Based on the historical baseline load data of residential areas, an aggregate EV charging target curve is formulated based on the approximation of load variance minimization objective function. Subsequently, optimized charging plans for each arriving vehicle are dynamically generated according to user flexibility. By adjusting charging power in real-time while meeting users' charging demands, the method seeks to closely track the optimized load target, thereby reducing both peak load and overall load variance. Experimental results demonstrate that the proposed strategy achieves high online computational efficiency, and the generating time of the optimized charging plan for a single vehicle is 14 to 16 milliseconds, which supports rapid real-time interaction with users, and thus attracts more users to participate in charging regulation. Compared to uncontrolled charging, participating users of real-time charging control had an average charging delay of 3.33 hours, leading to an 11% reduction in the overall load variance of the residential area.

Key words: electric vehicles, charging strategy, peak load shaving and valley filling, charging load, user interaction

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