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Prediction of performance measures for buses: A system-based approach.

机译:公交车性能指标的预测:一种基于系统的方法。

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摘要

Heavy vehicles transport people and freight in an efficient manner and form the backbone of any developed economy. Historically, heavy vehicle standards (called Design Based Standards-DBS) have relied primarily on placing limits on vehicle weights and dimensions. Ease of implementation and lack of complete understanding of the complex relationships between vehicle design parameters and vehicle performance are the primary reasons such standards have been in place for decades. However, recently there has been a realization that such indirect control of vehicle performance can lead to a wide gap between intended performance and actual performance. A concept called Performance Based Standards (PBS) that assigns numerical limits to performance measures but leaves open ways of achieving the performance has now gained attention. However, for an effective implementation of PBS and for an optimal design of vehicles capable of achieving such performance, it is imperative that vehicle designers, test engineers and regulatory authorities alike have a sound understanding of the complex relationships that exist between vehicle design parameters and vehicle performance.; The primary goal of this thesis was to establish and validate methods of analysis that can be used to investigate the relationships between vehicle design and performance. The other main goals include development of a reliable means of predicting the useful range of values for vehicle design parameters that would result in a vehicle with desired performance objectives (one or more) and evaluation of the effectiveness of PTI (Pennsylvania Transportation Institute) testing with an aim to suggest ways for further improvement. The thesis discusses four performance measures, namely fuel economy, acceleration and gradeability, pass-by noise and vehicle reliability primarily because PTI data were readily available for these measures. However, as the methods developed in this thesis are very general in nature, they can be used for analyzing other vehicle performance measures as well, provided reliable and accurate data are available.; In this thesis, a two-stage system based model was implemented. In the first stage, functional relationships between vehicle design parameters and vehicle performance under laboratory/test track conditions were modeled using a vehicle transformation, F. In the second stage, the interdependence between test-track performance and real-life traffic performance was modeled using a traffic transformation, G. PTI data on fuel economy, acceleration and gradeability, pass-by noise and reliability for 124 diesel two-axle transit buses were used to model the vehicle transformation, F. On the other hand, the traffic transformation, G, was modeled using in-use data (at transit agencies) on fuel economy and reliability obtained from the National Transit Database (NTD).; Modeling was done using two artificial neural network (ANN) based methods---N2PFA/REFANN proposed by Setiono et al. and RF5 proposed by Saito and Nakano. Both approaches have built-in rule generation capabilities and an automatic means for selection of the number of hidden neurons. Hence the problem of ANN being a black box is alleviated with either approach. A two-step method of input selection was used in this study. Firstly, correlation coefficients were evaluated between all pairs of input variables. Based on the assumption that a low correlation coefficient (0.7) can be used as a measure of linear independence, inputs that are least correlated to other inputs were selected. In the second step, an information theory based approach was used to narrow this input set down to a still smaller subset that contains most of the original information and is a good predictor of the output (vehicle performance measure) under study.; Often it is also of interest to know vehicle configurations that can achieve desired vehicle performance objectives. To this effect, a means for generating an inverse model
机译:重型车辆可以高效地运送人员和货物,并构成任何发达经济体的骨干。从历史上看,重型车辆标准(称为基于设计的标准DBS)主要依靠对车辆重量和尺寸进行限制。易于实施和缺乏对车辆设计参数与车辆性能之间复杂关系的全面理解是此类标准已经实施数十年的主要原因。然而,近来已经意识到,对车辆性能的这种间接控制会导致预期性能与实际性能之间的巨大差距。一种称为“基于性能的标准(PBS)”的概念现在为关注性能的方法分配了数值限制,但留下了实现性能的开放方式。但是,为了有效实施PBS并优化能够实现这种性能的车辆,必须使车辆设计人员,测试工程师和监管机构都对车辆设计参数和车辆之间存在的复杂关系有深刻的了解。性能。;本文的主要目的是建立和验证可用于研究车辆设计和性能之间关系的分析方法。其他主要目标包括开发一种可靠的方法,以预测车辆设计参数的有用值范围,从而使车辆具有所需的性能目标(一个或多个),并评估PTI(宾夕法尼亚州运输研究所)测试的有效性目的是提出进一步改进的方法。本文讨论了四个性能指标,即燃油经济性,加速度和坡度,路过噪声和车辆可靠性,这主要是因为PTI数据可随时用于这些指标。但是,由于本论文开发的方法本质上非常通用,因此,只要有可靠,准确的数据,它们也可以用于分析其他车辆性能指标。本文提出了一种基于两阶段系统的模型。在第一阶段,使用车辆变换F对车辆设计参数与实验室/测试轨道条件下的车辆性能之间的功能关系进行建模。在第二阶段,使用模型对测试轨道性能与现实交通性能之间的相互依赖性进行建模。 PTI的有关燃油经济性,加速和坡度,路过噪声和可靠性的124柴油两轴公交车的PTI数据用于建模车辆变换F。另一方面,交通变换G ,是使用从国家公交数据库(NTD)获得的关于燃油经济性和可靠性的在用数据(在运输机构中)建模的;建模是使用Setiono等人提出的两种基于人工神经网络(ANN)的方法-N2PFA / REFANN进行的。 Saito和Nakano提出的RF5。两种方法都具有内置的规则生成功能和用于选择隐藏神经元数量的自动方法。因此,两种方法都可以缓解ANN是黑匣子的问题。本研究使用两步法进行输入选择。首先,在所有输入变量对之间评估相关系数。基于低相关系数(<0.7)可以用作线性独立性度量的假设,选择了与其他输入相关性最低的输入。第二步,使用基于信息论的方法将输入范围缩小到一个较小的子集,该子集包含大多数原始信息,并且可以很好地预测所研究的输出(车辆性能指标)。人们通常也很感兴趣知道可以实现期望的车辆性能目标的车辆配置。为此,一种用于生成逆模型的方法

著录项

  • 作者

    Muthiah, Saravanan.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Engineering Automotive.; Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 243 p.
  • 总页数 243
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术及设备;机械、仪表工业;
  • 关键词

  • 入库时间 2022-08-17 11:40:59

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