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首页> 外文期刊>Energy >Data-driven hierarchical control for online energy management of plug-in hybrid electric city bus
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Data-driven hierarchical control for online energy management of plug-in hybrid electric city bus

机译：数据驱动的分层控制用于插电式混合动力城市公交车的在线能源管理

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摘要

The pre-determined city bus routes and the availability of partial-trip information obtained through vehicular connectivity provides new opportunities for plug-in vehicles to plan electric energy reasonably. This paper presents a data-driven hierarchical control method for online energy management of plug-in hybrid electric city buses, which can learn from globally optimal solutions based on historical accumulated cycles while taking advantage of connectivity-enabled partial-trip information. The devised scheme comprises two levels of control modules. The upper battery state-of-charge planner trained using historical optimal data is employed for deriving a reference state-of-charge based on the current battery state, remaining trip length, and low/high speed ratios. The lower powertrain controller is then applied to regulate the engine operation according to the reference state-of-charge and powertrain states. This article presents two contributions: (1) both accumulated historical optimal data and partial trip information are assimilated to augment the applicability of the control hierarchy, thus achieving better resilience to "unseen" driving patterns; (2) given limited resources of micro-controllers, the control strategy is proven to be a real-time implementable, close-to-optimal solution. A variety of results show that the proposed approach can achieve significant fuel savings (4.99%-14.80%) as compared to the charge depleting and charge sustaining strategy. (C) 2017 Elsevier Ltd. All rights reserved.
机译：预定的城市公交路线以及通过车辆连接获得的部分出行信息的可用性为插电式车辆合理规划电能提供了新的机会。本文提出了一种数据驱动的分层控制方法，用于插电式混合动力城市公交车的在线能源管理，该方法可以在基于历史累积周期的基础上，从全球最优解决方案中学习，同时利用具有连通性的局部行程信息。设计方案包括两级控制模块。使用历史最佳数据训练的上层电池充电状态计划器，用于根据当前电池状态，剩余行程长度和低速/高速比得出参考充电状态。然后，将下部动力总成控制器应用于根据参考荷电状态和动力总成状态来调节发动机运行。本文提出了两个方面的贡献：（1）积累的历史最佳数据和部分行程信息都被同化，以增强控制层次结构的适用性，从而更好地适应“看不见的”驾驶模式； （2）在给定的微控制器资源有限的情况下，控制策略被证明是一种实时可行的，接近最优的解决方案。各种结果表明，与电荷消耗和电荷维持策略相比，该方法可以节省大量燃料（4.99％-14.80％）。 （C）2017 Elsevier Ltd.保留所有权利。

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  • 来源
    《Energy》 |2018年第1期|55-67|共13页
  • 作者

    Tian He; Li Shengbo Eben; Wang Xu; Huang Yong; Tian Guangyu;

  • 作者单位

    Tsinghua Univ, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China;

    Tsinghua Univ, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China;

    Tsinghua Univ, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China;

    Tsinghua Univ, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China;

    Tsinghua Univ, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    City bus; Plug-in hybrid; Data driven; Vehicle connectivity; Online energy management;

    机译：城市公交;插电式混合动力;数据驱动;车辆连接;在线能源管理;

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