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Model-based estimation for in-cylinder pressure of advanced combustion engines.

机译：基于模型的高级内燃机缸内压力估算。

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摘要

Cylinder pressure is one of the most important parameters that characterize the combustion process in an internal combustion engine. Recent developments in engine control technologies suggest the use of cylinder pressure as a feedback signal for closed-loop combustion control. However, the sensors measuring in-cylinder pressure are typically subject to noise and offset issues, requiring signal processing methods to be applied to obtain a sufficiently accurate pressure trace. The signal conditioning implies a considerable computational burden, which ultimately limits the use of cylinder pressure sensing to laboratory testing, where the signal can be processed off-line.;In order to enable closed-loop combustion control through cylinder pressure feedback, a real-time algorithm that extracts the pressure signal from the in-cylinder production grade sensor is proposed in this study. The algorithm is based on a crank-angle based engine combustion that predicts the in-cylinder pressure from the definition of a burn rate function. The model is then adapted to model-based estimation by applying an extended Kalman filter in conjunction with a recursive least squares estimation scheme. The estimator is tested at certain operating points on a high-fidelity Diesel engine simulator, as well as on experimental data obtained at various operating conditions. The results obtained show the effectiveness of the estimator in reconstructing the cylinder pressure on a crank-angle basis and in rejecting measurement noise and modeling errors. Furthermore, a comparative study with a conventional signal processing method shows the advantage of using the derived estimator, especially in the presence of high signal noise (as frequently happens with low-cost sensors).;As an extension and further application, this methodology is built upon to cover a wider range of operations as well as transient data. Linear parameter varying techniques using genetic algorithms are utilized to identify the gains of linear spline functions of the LPV-corrector estimator. The LPV-corrector performs well with a relatively small computation burden. The two estimators are examined under both steady state data and transient data, where the comparison criteria include estimation of combustion metrics.;Finally, a model-based estimation methodology that facilitates real-time reconstruction of individual in-cylinder pressure utilizing a minimum sensor set is demonstrated. Based on a derived crankshaft speed model incorporated with the pressure model, a sliding mode observer is implemented, wherein chattering is mitigated and the estimation design is validated. Adding disturbances to the model parameter degrades the performance of the SMO, which motivates the development of an adaptive-SMO based on the certainty equivalence principle, utilizing the cylinder pressure signal from one cylinder. The estimator was derived analytically and a proof of stability is provided.
机译：气缸压力是表征内燃机燃烧过程的最重要参数之一。发动机控制技术的最新发展表明，将气缸压力用作闭环燃烧控制的反馈信号。但是，测量缸内压力的传感器通常会遇到噪声和偏移问题，需要采用信号处理方法来获得足够准确的压力曲线。信号调理意味着大量的计算负担，最终将气缸压力感测的使用限制在实验室测试中，在实验室测试中可以离线处理信号。为了通过气缸压力反馈实现闭环燃烧控制，提出了一种从缸内生产等级传感器中提取压力信号的时间算法。该算法基于基于曲轴角的发动机燃烧，该燃烧根据燃烧率函数的定义预测缸内压力。然后，通过结合递归最小二乘估计方案应用扩展的卡尔曼滤波器，使模型适用于基于模型的估计。该估算器在高保真柴油发动机模拟器上的某些操作点上进行测试，并在各种操作条件下获得的实验数据上进行测试。所获得的结果表明，估计器在重建曲柄角上的气缸压力以及排除测量噪声和建模误差方面是有效的。此外，与常规信号处理方法的比较研究表明使用派生估计器的优势，特别是在存在高信号噪声的情况下（低成本传感器经常发生这种情况）。作为一种扩展和进一步的应用，这种方法是建立在涵盖更广泛的操作以及瞬态数据的基础上。利用遗传算法的线性参数变化技术可用于识别LPV校正器估计器的线性样条函数的增益。 LPV校正器以相对较小的计算负担表现良好。在稳态数据和瞬态数据下都检查了这两个估计器，其中比较标准包括燃烧指标的估计。最后，基于模型的估计方法有助于利用最小传感器集实时重建各个缸内压力。被证明。基于推导的曲轴速度模型和压力模型，实现了滑模观测器，可以减轻抖动并验证估计设计。将干扰添加到模型参数会降低SMO的性能，从而利用确定性等效原理，利用来自一个汽缸的汽缸压力信号，来推动自适应SMO的发展。该估计量是通过分析得出的，并提供了稳定性证明。

著录项

  • 作者

    Al-Durra, Ahmed Abad.;

  • 作者单位

    The Ohio State University.;

  • 授予单位 The Ohio State University.;
  • 学科 Engineering Electronics and Electrical.;Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 174 p.
  • 总页数 174
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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