Large-Scale Adaptive Electric Vehicle Charging

Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, S. Low
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引用次数: 44

Abstract

Large-scale charging infrastructure will play an important role in supporting the adoption of electric vehicles. In this paper, we address the prohibitively high capital cost of installing large numbers of charging stations within a parking facility by oversubscribing key pieces of electrical infrastructure. We describe a unique physical testbed for large-scale, high- density EV charging research which we call the Adaptive Charging Network (ACN). We describe the architecture of the ACN including its hardware and software components. We also present a practical framework for online scheduling, which is based on model predictive control and convex optimization. Based on our experience with practical EV charging systems, we introduce constraints to the EV charging problem which have not been considered in the literature, such as those imposed by unbalanced three-phase infrastructure. We use simulations based on real data collected from the ACN to illustrate the trade-offs involved in selecting models for infrastructure constraints and accounting for non-ideal charging behavior.
大规模自适应电动汽车充电
大规模的充电基础设施将在支持电动汽车的采用方面发挥重要作用。在本文中,我们通过超额认购电力基础设施的关键部分来解决在停车设施内安装大量充电站的过高资本成本问题。本文描述了一种用于大规模、高密度电动汽车充电研究的独特物理测试平台——自适应充电网络(ACN)。我们描述了ACN的架构,包括它的硬件和软件组件。提出了一种基于模型预测控制和凸优化的在线调度实用框架。根据我们对实际电动汽车充电系统的经验,我们引入了文献中未考虑的电动汽车充电问题的约束,例如不平衡三相基础设施所施加的约束。我们使用基于从ACN收集的真实数据的模拟来说明选择基础设施约束模型和考虑非理想充电行为所涉及的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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