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It is commonly accepted that future powertrains will be based to a large extent on hybrid architectures, in order to optimize fuel efficiency and reduce CO₂ emissions. Hybrid operation is typically achieved with frequent engine start-and-stops during real-world as well as during the legislated driving cycles. The cooling of the exhaust system during engine stop may pose problems if the substrate temperature drops below the light-off temperature. Therefore, the design and thermal management of after-treatment systems for hybrid applications should consider the 3-dimensional heat transfer problem carefully. On the other hand, the after-treatment system calculation in the concept design phase is closely linked with engine calibration, taking into account the hybridization strategy. Therefore, there is a strong need to couple engine simulation with 3d aftertreatment predictions.

The pressure for compact and efficient deNO systems has led to increased interest of incorporating SCR coatings in the DPF walls. This technology could be very attractive especially if high amounts of washcoat loadings could be impregnated in the DPF porous walls, which is only possible with high porosity filters. To counterbalance the filtration and backpressure drawbacks from such high porosity applications, the layered wall technology has already been proposed towards minimizing soot penetration in the wall and maximizing filtration efficiency. In order to deal with the understanding of the complex interactions in such advanced systems and assist their design optimization, this paper presents an advanced modeling framework and selected results from simulation studies trying to illustrate the governing phenomena affecting deNO performance and passive DPF regeneration in the above combined systems.

The successful design and especially the control of the SCR system is a challenging process that can be supported by the application of simulation tools. As a first step, we employ physico-chemically informed ‘off-line’ models that are calibrated with the help of targeted small- and full-scale tests. Despite their high level of sophistication, this SCR model is able to be integrated in a control-oriented simulation software platform and connected to other powertrain simulation blocks. The target is to use this simulation platform as a virtual environment for the development and optimization of SCR control strategies. The above process is demonstrated in the case of a passenger car SCR. The model is calibrated at both fresh and aged catalyst condition and validated using experimental data from the engine bench under a wide variety of operating conditions. Next, the calibrated model was coupled with embedded control models, developed for Euro 6 passenger car powertrains.

The objective of this study is to present a particulate filter concept, based on a new porous material: INCOFOAM® HighTemp, a Ni-based superalloy foam. The paper examines the filtration and pressure drop characteristics as well as the regeneration performance of different filter configurations, based on experimental data and modeling. A number of different foam structures with variable pore characteristics are studied. The experimental testing covers flow and pressure drop behavior with air and exhaust gas, filtration efficiency measurements as function of particle size and regeneration rate measurements. The testing starts from mini-scale reactors and proceeds to real exhaust testing on the engine bench as well as vehicle tests on the chassis dynamometer and on-road. In parallel, a previously developed mathematical model is applied to study and understand the filtration and pressure drop mechanisms in the case of clean and soot loaded filters.

In Plug in hybrid electric vehicles (PHEVs), the management of the main drivetrain components and the shift between pure electric and hybrid propulsion is decided by the on-board energy management system (EMS). The EMS decisions have a direct impact on CO2 emissions and need to be optimized to achieve as low emissions as possible. This paper presents optimization methods for EMS algorithms of a parallel P2 PHEV. Two different supervisory control algorithms are examined, employing simulations on a validated PHEV platform. An Equivalent Consumption Minimization Strategy (ECMS) algorithm is implemented and compared to a rule-based one, the latter derived by back-engineering of available experimental data. The different EMS algorithms are analyzed and compared on an equal basis in terms of distance, demanded energy and state of charge levels over different driving cycles.