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This paper discusses some specific data-driven model structures suitable for prediction of NOX and soot emissions from a diesel engine. The model structures can be described as local linear regression models where the regression parameters are defined by two-dimensional look-up tables. It is highlighted that this structure can be interpreted as a B-spline function. Using the model structure, models are derived from measured engine data. The smoothness of the derived models is controlled by using an additional regularization term and the globally optimal model parameters can be found by solving a linear least-squares problem. Experimental data from a 5-cylinder Volvo passenger car diesel engine is used to derive NOX and soot models, using a leave-one-out cross validation strategy to determine the optimal degree of regularization. The model for NOX emissions predicts the NOX mass flow with an average relative error of 5.1% and the model for soot emissions predicts the soot mass flow with an average relative error of 29% for the measurement data used in this study. The behavior of the models for different engine management system settings regarding boost pressure, amount of exhaust gas recirculation, and injection timing has been studied. The models react to the different engine management system settings in an expected way, making them suitable for optimization of engine management system settings. Finally, the model performance dependence on the selected model complexity, and on the number of measurement data points used to derive the models has been studied.

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Citation for the published paper: Grahn, M. ; Johansson, K. ; McKelvey, T. (2014) "Model-based diesel Engine Management System optimization for transient engine operation". Control Engineering Practice, vol. 29 pp. 103-114.http://dx. a b s t r a c tA recently developed strategy to calculate set points for controllable diesel engine systems is described, further developed, and evaluated. The strategy calculates set points with an aim to minimize fuel consumption for a given dynamic vehicle driving cycle, while keeping accumulated emissions below given limits. The strategy is based on existing methodology for steady-state engine operation, but extended to handle transient effects in the engine caused by dynamics in the engine air system. Using the strategy, set points for the complete operating range of the engine can be calculated off-line and stored in an Engine Management System, hence set points can be derived for any (steady-state or transient) driving scenario. The strategy has been evaluated using a simulation model of a complete diesel engine vehicle system. The model estimates fuel consumption, NO X , and soot emissions for a dynamic vehicle driving cycle depending on set points for boost pressure, oxygen fraction in the intake manifold, and injection timing, throughout the simulation. Using this simulation model, the strategy has been shown to decrease fuel consumption for the New European Driving Cycle with 0.56%, the Federal Test Procedure with 1.04%, and the Japanese JC08 cycle with 0.84% compared to a strategy based on steady-state engine operation.

Systems engineers face the ever increasing chase for reduced time to market, while the systems to develop ever increase in complexity. Software systems design and integration processes have therefor evolved along the well-known V-cycle. This paper will focus on the software integration for mechatronic systems as they develop fast due to high demands and challenging requirements in the automotive industry. The development order of model in the loop (MIL), software in the loop (SIL), processor in the loop (PIL) and hardware in the loop (HIL) can be seen as state of the art practised by many systems engineers. Driver in the loop (DIL) may be in its infancy, but rapidly growing. The novelty presented in this paper is the consistency of the plant models used in the integration chain supporting consistent model data propagation: Functional Mock-up Units (FMU) defined by the open standard of the Functional Mock-up Interface 1 (FMI).