Cylinder pressure-based control is a key enabler for advanced pre-mixed combustion concepts. Besides guaranteeing robust and safe operation, it allows for cylinder pressure and heat release shaping. This requires fast control-oriented combustion models. Over the years, mean-value models have been proposed that can predict combustion measures (e.g., Gross Indicated Mean Effective Pressure, or the crank angle where 50% of the total heat is released) or models that predict the full in-cylinder pressure. However, these models are not able to capture cyclic variations. This is important in the control design for combustion concepts, like Reactivity Controlled Compression Ignition, that can suffer from large cyclic variations. In this study, the in-cylinder pressure and cyclic variation are modelled using a data-based approach. The model combines Principle Component Decomposition and Gaussian Process Regression. A detailed study is performed on the effects of the different hyperparameters and kernel choices. The approach is applicable to any combustion concept, but most valuable for advance combustion concepts with large cyclic variation. The potential of the proposed approach is demonstrated for an Reactivity Controlled Compression Ignition engine running on Diesel and E85. The prediction quality of the evaluated combustion measures has an overall accuracy of 13.5% and 65.5% in mean behaviour and standard deviation, respectively. The peak-pressure rise-rate is traditionally hard to predict, in the proposed model it has an accuracy of 22.7% and 96.4% in mean behaviour and standard deviation, respectively. This Principle Component Decomposition-based approach is an important step towards in-cylinder pressure shaping. The use of Gaussian Process Regression provides important information on cyclic variation and provides next-cycle controls information on safety and performance criteria.

翻译：基于缸内压力的控制是实现先进预混燃烧概念的关键推动因素。除了保证稳健和安全的运行外，它还允许对缸内压力和放热进行整形。这需要快速的控制导向燃烧模型。多年来，提出了能够预测燃烧指标（例如，总指示平均有效压力，或总热量释放50%时的曲轴转角）的平均值模型，或预测整个缸内压力的模型。然而，这些模型无法捕捉循环变动。这对于像反应性控制压缩点火（RCCI）这类可能遭受较大循环变动的燃烧概念的控制设计至关重要。在本研究中，采用基于数据的方法对缸内压力和循环变动进行建模。该模型结合了主成分分解（Principal Component Decomposition, PCD）和高斯过程回归（Gaussian Process Regression, GPR）。对不同的超参数和核函数选择的影响进行了详细研究。该方法适用于任何燃烧概念，但对于具有较大循环变动的先进燃烧概念最有价值。针对一台使用柴油和E85运行的RCCI发动机，展示了所提出方法的潜力。所评估燃烧指标的预测质量在平均行为和标准差方面总体精度分别为13.5%和65.5%。传统上难以预测的峰值压力上升率在该模型中在平均行为和标准差方面的精度分别达到22.7%和96.4%。这种基于主成分分解的方法是实现缸内压力整形的重要一步。使用高斯过程回归提供了关于循环变动的重要信息，并为下一循环的控制提供了有关安全性和性能标准的信息。