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Comparison of Sensor Configurations for Mass Flow Estimation of Turbocharged Diesel Engines

机译:涡轮增压柴油机质量流量估计传感器配置的比较

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摘要

The increasing demands on quality of power, emissions and overall performance of combustion engines lay new goals for the hardware and the software development of control systems. High-performance embedded controllers open the possibilities for application of numerical methods to solve the problems of modeling and control of combustion engines. Algorithms for estimation of state and parameters are essential components of many advanced control, monitoring and signal processing engine applications. A widely applicable estimator is given by the Extended Kalman Filter (EKF) which defines a finite memory recursive algorithm suited for real-time implementation, where only the last measurement is used to update the state estimate, based on the past history being approximately summarized by the a priori estimate of the state and the error covariance matrix estimate. The proposed EKF uses an augmented air-path state-space model to estimate unmeasurable mass flow quantities. The EKF algorithm based on the augmented state-space model considerably reduces the modeling errors compared to the open loop estimator simulation and compared to the EKF without the augmentation which is demonstrated on a standard production Diesel engine data. The experimental validation of the observed state quantities is performed against the measured pressures and turbocharger speed dataf The estimated mass flow quantities are indirectly validated through the air compressor flow that is directly validated against air mass flow sensor data. Two two-sensor setups are considered in this study. In the first experiment the intake manifold pressure and the turbocharger speed is used. The second experiment uses the intake manifold pressure and the exhaust manifold pressure as the measurement information for the EKF. The second experiment gives more precise mass flow estimate in term of less bias on the estimates, but more variance due to the high frequency exhaust manifold pressure variations caused by the exhaust valves.
机译:对动力质量,排放和内燃机整体性能的日益增长的要求为控制系统的硬件和软件开发提出了新的目标。高性能嵌入式控制器为解决内燃机建模和控制问题提供了数值方法应用的可能性。状态和参数的估计算法是许多高级控制,监视和信号处理引擎应用程序的基本组成部分。扩展卡尔曼滤波器(EKF)给出了一种广泛适用的估计器,它定义了一种适用于实时实现的有限内存递归算法,其中仅基于过去的历史大致总结了过去的测量,才使用最后的测量值来更新状态估计。状态的先验估计和误差协方差矩阵估计。拟议的EKF使用增强的空气路径状态空间模型来估算不可测量的质量流量。与开环估计器仿真相比,与不带增强的EKF相比,基于增强状态空间模型的EKF算法大大减少了建模误差,这在标准生产的柴油机数据上得到了证明。相对于测得的压力和涡轮增压器转速数据f对观察到的状态量进行实验验证。通过直接针对空气质量流量传感器数据验证的空气压缩机流量间接验证估算的质量流量。在这项研究中考虑了两个双传感器设置。在第一个实验中,使用进气歧管压力和涡轮增压器速度。第二个实验使用进气歧管压力和排气歧管压力作为EKF的测量信息。第二个实验给出了更精确的质量流量估算值,因为估算值的偏差较小,但是由于排气阀引起的高频排气歧管压力变化而产生的偏差更大。

著录项

  • 来源
    《Identification for automotive systems》|2010年|p.303-326|共24页
  • 会议地点 Linz(AT)
  • 作者单位

    Institute of Automation, Measurement and Applied Informatics, Faculty of Mechanical Engineering, Slovak University of Technology, Bratislava, Slovakia;

    Institute of Automation, Measurement and Applied Informatics, Faculty of Mechanical Engineering, Slovak University of Technology, Bratislava, Slovakia;

    Institute for Design and Control of Mechatronical Systems, Johannes Kepler University, Linz, Austria;

    Institute for Design and Control of Mechatronical Systems, Johannes Kepler University, Linz, Austria;

    Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 汽车工程;
  • 关键词

  • 入库时间 2022-08-26 14:23:28

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