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Enhancing the predictive quality of engine models, while maintaining an affordable computational cost, is of great importance. In this study, a phenomenological combustion and a tabulated NO x model, focusing on efficient modeling and improvement of computational effort, is presented. The proposed approach employs physical and chemical sub-models for local processes such as injection, spray formation, ignition, combustion, and NO x formation, being based on detailed tabulated chemistry methods. The applied combustion model accounts for the turbulence-controlled as well as the chemistry-controlled combustion. The phenomenological combustion model is first assessed for passenger car application, especially with multiple pilot injections and high exhaust gas recirculation ratios for low-load operating points. The validation results are presented for representative operating conditions from a single-cylinder light-duty diesel engine and over the entire engine map of a heavy-duty diesel engine. In the second part of this study, a novel approach for accurate and very fast modeling of NO formation in combustion engines is proposed. The major focus of this study is on the development of a very fast-running NO mechanism for usage in the next generation of the engine control units. This approach is based on tabulation of a detailed chemical kinetic mechanism and is validated against the detailed chemical reaction mechanism at all engine-relevant conditions with the variation in pressure, temperature, and air-fuel ratio under stationary and ramp-type transient conditions in a perfectly stirred reactor. Using this approach, a very good match to the results from calculations with the detailed chemical mechanism is observed. Finally, the tabulated NO x kinetic model is implemented in the combustion model for in-cylinder NO x prediction and compared with the experimental engine measurement data.

<div class="section abstract"><div class="htmlview paragraph">One challenge for the development of commercial vehicles is the reduction of CO₂ greenhouse, where hydrogen can help to reduce the fleet CO₂. For instance, in Europe a drop in fleet consumption of 15% and 30% is set as target by the regulation until 2025 and 2030. Another challenge is EURO VII in EU or even already approved CARB HD Low NO_x Regulation in USA, not only for Diesel but also for hydrogen combustion engines.</div><div class="htmlview paragraph">In this study, first the requirements for the combustion and after-treatment system of a hydrogen engine are defined based on future emission regulations. The major advantages regarded to hydrogen combustion are due to the wide range of flammability and very high flame speed numbers compared to other fossil based fuels. Thus, it can be well used for lean burn combustion with much better fuel efficiency and very low NO_x emissions with an ultra lean combustion.</div><div class="htmlview paragraph">A comprehensive experimental investigation is performed on a HD 2 L single-cylinder engine. The hydrogen combustion characteristics are studied with variation of multiple operating parameters like EGR, air-fuel ratio, etc. A predictive hydrogen combustion and NO_x model is then developed and validated using the test results.</div><div class="htmlview paragraph">As baseline for the numerical investigations of engine transient behavior in the cold cycle, an in-line six cylinder 12L HD diesel engine is developed. Cold WHTC and FTP cycles are simulated and the combustion, exhaust gas temperature and emission behavior are evaluated.</div><div class="htmlview paragraph">The effects of lean-burning combustion and exhaust after-treatment for engine NO_x reduction as well as thermal management in transient cycles on exhaust after-treatment (EAT) system to fulfil future regulations are discussed. Multiple EAT architectures are investigated and the trade-off between fuel consumption and the end-of-pipe NO_x is assessed. Challenges and potentials of hydrogen combustion for heavy-duty applications considering future regulations are addressed. Variation of engine operating parameters and the potentials of engine calibration for series development is demonstrated using predictive engine and EAT models.</div></div>

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.