Exhaust aftertreatment modeling for efficient calibration in diesel passenger car applications

Interest in utilizing advanced lean-burn gasoline and diesel engines has increased in the last decades due to their reduced greenhouse gas emissions and increased fuel economy. One impediment to the increasing use of these engines, however, is the need to develop corresponding catalytic systems for controlling pollutant emissions. In particular, although still far from the fuel neutral United States (US) approach, European (EU) legislation limits for Nitrogen Oxides (NOx) emissions are becoming more and more severe and also type approval procedures are going to radically change with the introduction of Worldwide harmonized Light vehicles Test Cycle (WLTC) and Real Driving Emission (RDE) tests. Considering that test bench and chassis dyno experimental campaigns are costly and require a vast use of resources for the generation of data; therefore, reliable and computationally efficient simulation models are essential in order to identify the most promising technology mix to satisfy emission regulations and fully exploit advantages of diesel and lean-burn gasoline when minimizing the side effect of their emissions. Therefore, the aim of this work is to develop reliable models of the individual aftertreatment components and to calibrate the kinetic parameters based on experimental measurements which can be further used as a virtual test rig to evaluate the effectiveness of each technology in terms of reducing pollutant emissions. In the current work, a brief introduction regarding the passenger car emissions, regulations and control technologies, including in-cylinder control techniques and aftertreatment systems, is provided in Chapter 1. In addition, simulation modelling approaches for aftertreatment applications are discussed. More details about specific aftertreatment components are discussed in the next chapters. As an example, the modeling of a Selective Catalytic Reduction coated on Filter (SCR-F), on the basis of Synthetic Gas Bench (SGB) reactor data is presented in Chapter 2; focusing, in particular, on estimation of ammonia storage capacity, NOx conversion and soot reduction due to passive regeneration. LNT is analyzed in Chapter 3, focusing on the reactor-scale Synthetic Gas Bench (SGB) experiments and calibration of the 1D simulation model for two case studies with the aim to characterize Oxygen Storage Capacity (OSC), NOx Storage and Reduction (NSR) and light-off. The calibrated 1D simulation model is thereafter validated, in Chapter 4, for one of the case studies using engine-out emissions, mass flowrate and temperature traces over Worldwide harmonized Light vehicles Test Cycle (WLTC) as the boundary condition for the inlet of LNT for full-size component. Afterwards, the LNT model calibrated in Chapter 3 is, in Chapter 5, further reduced and linearized with reasonable assumptions to be used as a plant-model with very low computational requirement and in real time applications such as Electronic Control Unit (ECU)/ Hardware-in-the-Loop (HiL) systems. Finally, after discussing NOx control systems in previous chapters, modeling of Diesel Oxidation Catalyst (DOC), which plays a fundamental role not only for the CO and HC conversion, but also for promoting the oxidation of NO into NO2, is discussed in Chapter 6. It is worth noting that depending on the complexity of the kinetic model, different optimization tools are implemented for the calibration; as an example, Brent method is used for calibration of SCR-F kinetic model, likewise, Genetic Algorithm (GA) is used for the calibration of the DOC kinetic parameters; however, for more complex kinetic schemes like LNT both manual and automatic optimization is required to evaluate the most suitable reaction pathways and kinetic parameters. Accordingly, after development of the kinetic model for each aftertreatment component and validation of the full-scale model, further investigations could be devoted to combining the models in order to simulate the whole aftertreatment system and assess the performance over different driving cycles.