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：基于缸压的控制是实现先进预混燃烧概念的关键技术。除了保证稳健安全运行外，它还能实现缸压与放热率的波形调控，这需要快速面向控制的燃烧模型。多年来，学者们提出了可预测燃烧指标（如总指示平均有效压力或累计放热50%对应的曲轴转角）的平均值模型，以及预测全缸压曲线的模型。然而，这些模型无法捕捉循环变动——这对反应活性控制压燃等易出现大幅循环变动的燃烧概念的控制设计至关重要。本研究采用数据驱动方法对缸压及循环变动进行建模，该模型融合主成分分解与高斯过程回归技术。我们系统研究了不同超参数与核函数选择的影响。该方法适用于任何燃烧概念，但对存在大幅循环变动的先进燃烧概念价值最高。以柴油和E85燃料的RCCI发动机为例验证了所提方法潜力：评估的燃烧指标预测质量在均值行为和标准差方面分别达到13.5%和65.5%的总体精度；传统难预测的峰值压力升高率，在本模型中的均值行为和标准差精度分别达到22.7%和96.4%。这种基于主成分分解的方法是实现缸压波形调控的重要突破。高斯过程回归的使用不仅提供了循环变动的关键信息，还为下一循环的控制提供了安全与性能准则方面的依据。