上海交通大学学报

上海交通大学学报

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数据-模型混合驱动的电动汽车集群行为模拟方法（网络首发）

  

  1. 1.国网福建省电力有限公司经济技术研究院；2.上海交通大学电子信息与电气工程学院
  • 基金资助:
    国网福建省电力有限公司科技项目资助（52130N23000A）

Research on Electric Vehicle Fleet Behavior based on Data- and Model-Driven Simulation Method

  1. (1. State Grid Fujian Electric Power Co., Ltd. Economic and Technological Research Institute, Fuzhou, 350012, China；2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

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摘要： 针对实际电动汽车充电负荷数据缺乏、地区差异大、模拟方法复杂等问题，提出了由行程链、能耗链和充电链构成的电动汽车行为模拟方法。首先，为了解决实际充电数据缺乏的问题，采用数据驱动方式，基于高斯混合模型推导出车辆行程链构建方法和构建过程，根据电动汽车和常规汽车行程相似性，得到电动汽车的出行规律；然后，总结电动汽车行驶过程中的能耗模型，从而在行程链的基础上，得到电动汽车的能耗链；最后，综合考虑充电焦虑模型、用户排队情况、充电时间等因素，推导并建立了电动汽车的充电链。针对常见的充电策略进行了模拟，验证了不同充电策略对于用户充电成本和电网的影响。

关键词: 电动汽车, 行程链, 能耗链, 充电链, 高斯混合模型

Abstract: To address issues like lack of actual electric vehicle (EV) charging load data, significant regional differences, and complex simulation methods, we propose an EV behavior simulation method consisting of trip chains, energy consumption chains, and charging chains. Initially, to tackle the scarcity of charging data, we derive a vehicle trip chain construction method using a data-driven approach, leveraging similarities between EV and conventional vehicle trips to understand EV travel patterns. Subsequently, we summarize energy consumption models, creating energy consumption chains based on trip chains. Lastly, considering factors like charging anxiety, queueing, and charging time, we develop the EV charging chain. Simulations of common charging strategies demonstrate their impact on user charging costs and the power grid.

Key words: electric vehicle(EV), trip chain, energy consumption chain, charging chain, Gaussian mixture model (GMM)

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