The need for precise control of complex air handling systems on modern engines has driven research into model-based methods. While model-based control can provide improved performance over prior map-based methods, they require the creation of an accurate model. Physics-based models can be precise, but can also be computationally expensive and require extensive calibration. To address this limitation, this work explores the integration of data-driven models into an overall physics-based framework and applies this approach to the gas exchange processes of a diesel engine with a variable geometry turbocharger and exhaust gas recirculation. One of the most complex parts of this gas exchange loop is the turbocharger. Data-driven methods are used to capture the turbocharger performance and are also applied to the intake manifold, while the simpler features are captured with more traditional physics-based models. This combined modeling approach is able to capture the temperature and pressure dynamics with varying error levels depending on measurement availability and the inter-dependency of the submodels, with the turbocharger neural network model achieving a Normalized Mean Square Error (NMSE) of 5e-5 and the overall engine model achieving a NMSE of 4.5e-3. The work illustrates that the integration of data-driven models can improve overall model accuracy and may be able to reduce the number of sensors needed on the system. The contributions of this work are the development and demonstration of a neural network based turbocharger model and intake air path model, the development of empirical equation-based models for the rest of the engine components along the air path and the demonstration of the integration and interaction of these two types of model to adequately characterize engine operation for control applications.

对现代发动机上复杂空气处理系统的精确控制的需求推动了对基于模型的方法的研究。虽然基于模型的控制可以提供比先前基于地图的方法更高的性能，但它们需要创建一个准确的模型。基于物理的模型可能很精确，但计算成本也可能很高，并且需要大量校准。为了解决这一限制，这项工作探索了将数据驱动模型集成到基于物理的整体框架中，并将这种方法应用于具有可变几何涡轮增压器和废气再循环的柴油发动机的气体交换过程。该气体交换回路中最复杂的部分之一是涡轮增压器。数据驱动的方法用于捕捉涡轮增压器的性能，也适用于进气歧管，而更简单的特征是用更传统的基于物理的模型来捕捉的。这种组合建模方法能够根据测量可用性和子模型的相互依赖性捕获具有不同误差级别的温度和压力动态，涡轮增压器神经网络模型实现了 5e-5 的归一化均方误差 (NMSE) 和整体发动机模型达到 4.5e-3 的 NMSE。这项工作表明，数据驱动模型的集成可以提高整体模型的准确性，并可能减少系统所需的传感器数量。这项工作的贡献是基于神经网络的涡轮增压器模型和进气路径模型的开发和演示，