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Development of a Simulation based Powertrain Design Framework for Evaluation of Transient Soot Emissions from Diesel Engine Vehicles.

机译：基于仿真的动力总成设计框架的开发，用于评估柴油发动机车辆的瞬态烟尘排放。

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摘要

This dissertation presents the development of a modeling and simulation framework for diesel engine vehicles to enable soot emissions as a constraint in powertrain design and control. To this end, numerically efficient models for predicting temporally-resolved transient soot emissions are identified in the form of a third-order dual-input single-output (DISO) Volterra series from transient soot data recorded by integrating real-time (RT) vehicle level models in Engine-in-the-loop (EIL) experiments. It is shown that the prediction accuracy of transient soot significantly improves over the steady-state maps, while the model remains computationally efficient for systems-level work.;The evaluation of powertrain design also requires a systematic procedure for dealing with the issue that drivers potentially adapt their driving styles to a given design. In order to evaluate the implications of different powertrain design changes on transient soot production it is essential to compare these design changes on a consistent basis. This problem is explored in the context of longitudinal motion of a vehicle following a standard drive-cycle repeatedly. This dissertation develops a proportional-derivative (PD) type iterative learning based algorithm to synthesize driver actuator inputs that seek to minimize soot emissions using the Volterra series based transient soot models. The solution is compared to the one obtained using linear programming. Results show that about 19% reduction in total soot can be achieved for the powertrain design considered in about 40 iterations.;The two contributions of this dissertation: development of computationally efficient system level transient soot models and the synthesis of driver inputs via iterative learning for reducing soot, both contribute to improving the art of modeling and simulation for diesel powertrain design and control.
机译：本文提出了柴油机车辆建模和仿真框架的发展，以使烟尘排放成为动力总成设计和控制的约束。为此，通过集成实时（RT）车辆记录的瞬态烟尘数据，以三阶双输入单输出（DISO）Volterra系列的形式，确定了用于预测时间分辨瞬态烟尘排放的数值有效模型。在环引擎（EIL）实验中的水平模型。结果表明，瞬态烟灰的预测精度比稳态图显着提高，而该模型对于系统级的工作仍保持计算效率。;动力总成设计的评估还需要系统的程序来解决驾驶员潜在的问题根据特定设计调整驾驶风格。为了评估不同动力总成设计更改对瞬态碳烟产生的影响，必须在一致的基础上比较这些设计更改。在重复遵循标准驾驶循环的车辆的纵向运动的背景下探讨了这个问题。本文开发了一种基于比例-微分（PD）型迭代学习的算法，以合成驾驶员驱动器输入，从而使用基于Volterra系列的瞬态烟灰模型来寻求将烟灰排放量降至最低的方法。将该解决方案与使用线性编程获得的解决方案进行比较。结果表明，在大约40次迭代中考虑的动力总成设计可实现约19％的总烟灰减少;;本论文的两个贡献是：开发了计算效率高的系统级瞬态烟灰模型，以及通过迭代学习合成驾驶员输入减少烟灰，都有助于改善柴油机动力总成设计和控制的建模和仿真技术。

著录项

  • 作者

    Ahlawat, Rahul.;

  • 作者单位

    University of Michigan.;

  • 授予单位 University of Michigan.;
  • 学科 Engineering Automotive.;Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 167 p.
  • 总页数 167
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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