On the integration of physics-based and data-driven models for the prediction of gas exchange processes on a modern diesel engine

Abstract

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.