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A real-time pressure estimation algorithm for closed-loop combustion control

机译:用于闭环燃烧控制的实时压力估计算法

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The cylinder pressure is arguably the most important variable characterizing the combustion process in internal combustion engines. In light of the recent advances in combustion technologies and in engine control, the use of cylinder pressure is now frequently considered as a feedback signal for closed-loop combustion control algorithms. In order to generate an accurate pressure trace for real-time combustion control and diagnostics, the output of the in-cylinder pressure transducer must be conditioned with signal processing methods to mitigate the well-known issues of offset and noise. While several techniques have been proposed for processing the cylinder pressure signal with limited computational burden, most of the available methods still require one to apply low-pass filters or moving average windows in order to mitigate the noise. This ultimately limits the opportunity of exploiting the in-cylinder pressure feedback for a cycle-by-cycle control of the combustion process. To this extent, this paper presents an estimation algorithm that extracts the pressure signal from the in-cylinder sensor in real-time, allowing for estimating the 50% burn rate location and IMEP on a cycle-by-cycle basis. The proposed approach relies on a model-based estimation algorithm whose starting point is a crank-angle based engine combustion model that predicts the in-cylinder pressure from the definition of a burn rate function. Linear parameter varying (LPV) techniques are then used to expand the region of estimation to cover the engine operating map, as well as allowing for real-time cylinder estimation during transients. The estimator is tested on the experimental data collected on an engine dynamometer as well as on a high-fidelity engine simulator. 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, with considerably low computation effort.
机译:气缸压力可以说是表征内燃机燃烧过程的最重要的变量。鉴于燃烧技术和发动机控制的最新进展,现在经常将气缸压力的使用视为闭环燃烧控制算法的反馈信号。为了生成精确的压力曲线以进行实时燃烧控制和诊断,必须使用信号处理方法来调节缸内压力传感器的输出,以减轻众所周知的偏移和噪声问题。虽然已经提出了几种技术来以有限的计算负担来处理汽缸压力信号,但是大多数可用方法仍然需要一种方法来应用低通滤波器或移动平均窗口以减轻噪声。这最终限制了利用缸内压力反馈进行燃烧过程的逐周期控制的机会。就此而言,本文提出了一种估计算法,该算法可实时从缸内传感器提取压力信号,从而可以逐周期估计50%燃烧率位置和IMEP。所提出的方法依赖于基于模型的估计算法,该算法的起点是基于曲柄角的发动机燃烧模型,该模型根据燃烧率函数的定义预测缸内压力。然后,使用线性参数变化(LPV)技术来扩展估计范围以覆盖发动机运行图,并允许在瞬态过程中进行实时汽缸估计。估算器将根据在发动机测功机以及高保真发动机模拟器上收集的实验数据进行测试。所获得的结果表明,该估计器在以曲柄角为基础重建气缸压力,排除测量噪声和建模误差方面的有效性,而计算工作量却相当低。

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