• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Cost-Optimal Charging of Plug-In Hybrid Electric Vehicles Under Time-Varying Electricity Price Signals
【24h】

Cost-Optimal Charging of Plug-In Hybrid Electric Vehicles Under Time-Varying Electricity Price Signals

机译：时变电价信号下插电式混合动力汽车的成本最优充电

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper develops a convex quadratic programming (QP) framework for the charge pattern optimization of plug-in hybrid electric vehicles (PHEVs) under time-varying electricity price signals. The work is motivated by the need for a computationally efficient PHEV charging model in the bidirectional vehicle-to-grid (V2G) integration studies, accounting for the hybrid powertrain dynamics and battery energy losses of the PHEVs. We adopt a previously developed PHEV power management system and construct a simplified model for the convex optimization problem. We use an equivalent circuit battery model to compute battery energy losses during grid charging and discharging. We then derive the total fuel and electricity cost of the PHEV as a quadratic function of battery state of charge and use a standard QP solver to minimize it for a few sample trips obtained from the National Household Travel Survey data set. Using a quad-core computer, the daily PHEV charging trajectory with 5-min time resolution can be optimized in less than tenth of a second. Through several examples, we show the application of the proposed method in various V2G-related problems, such as obtaining the aggregate load patterns of PHEVs, analyzing the potential impacts of large-scale bidirectional V2G integration, benchmarking the fuel economy of PHEVs, and determining the sensitivity of V2G load to abrupt price variations.
机译：本文针对电价信号随时间变化的插电式混合动力汽车（PHEV）的充电模式优化，开发了凸二次规划（QP）框架。这项工作的动机是在双向车辆到电网（V2G）集成研究中需要一种计算有效的PHEV充电模型，该模型考虑了PHEV的混合动力总成动力学和电池能量损失。我们采用先前开发的PHEV电源管理系统，并针对凸优化问题构建简化模型。我们使用等效电路电池模型来计算电网充电和放电期间的电池能量损失。然后，我们将PHEV的总燃料和电力成本导出为电池充电状态的二次函数，并使用标准的QP解算器将其从国家家庭旅行调查数据集获得的一些样本行程中最小化。使用四核计算机，可以在不到十分之一秒的时间内优化具有5分钟时间分辨率的每日PHEV充电轨迹。通过几个示例，我们展示了该方法在各种V2G相关问题中的应用，例如获得PHEV的总负荷模式，分析大规模双向V2G集成的潜在影响，确定PHEV的燃油经济性并确定V2G负载对突然的价格波动的敏感性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号