Compliance with future greenhouse gas (GHG) and pollutant emissions poses major challenges for the further development of advanced internal combustion engines for light and heavy-duty applications. To meet all standards, the use of synthetic fuels and/or E-Fuels is an important alternative due to their enormous potential in terms of energy density, sustainability and low pollution combustion. However, the increased fuel diversity associated with the use of these alternative fuels adds an additional layer of complexity to the powertrain development and calibration process, resulting in an increase in development time and cost. This necessitates the application of advanced model-based closed-loop control strategies to optimize the fuel- and air-path calibration to make best use of the properties of the different fuels. One potential solution to address the impact of flex-fuel variances on the combustion process is the Combustion Rate Shaping (CRS) concept presented in this paper. It can maintain a desired combustion trace irrespective of fuel variations, hardware drifts and other external factors, thus ensuring optimal combustion. The highest benefit of this control concept comes from its application directly on the engine control unit (ECU) in combination with an in-cylinder measurement. This enables the online real-time control of the combustion, regardless of which fuel is being used. In addition, this also enables optimization and adaption of the fuel- and air-path settings to the used fuel while driving the vehicle. However, so far, the high computational cost of this control concept limits its real-time capability. Therefore, the optimization of the control concept to achieve real-time capability is the focus of this paper. The optimized control strategy is implemented on a demonstrator engine test bench built using a Rapid Control Prototyping (RCP) system. The controller performance is demonstrated both under steady-state and transient operating conditions, and the potential of the concept is demonstrated in several cases such as engine temperature variations, injector drift, individual cylinder balancing, and fuel-variation.

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The increasing connectivity of future vehicles allows the prediction of the powertrain operational profiles. This technology will improve the transient control of the engine and its exhaust gas aftertreatment systems. This article describes the development of a rule-based algorithm for the air path control, which uses the knowledge of upcoming driving events to reduce especially [Formula: see text] and particulate (soot) emissions. In the first section of this article, the boosting and the lean [Formula: see text] trap systems of a diesel powertrain are investigated as relevant sub-systems for shorter prediction horizons, suitable for Car-to-X communication range. Reference control strategies, based on state-of-the-art engine control unit algorithms and suitable predictive control logics, are compared for the two sub-systems in a model in the loop simulation environment. The simulation driving cycles are based on Worldwide harmonized Light-duty Test Cycle and Real Driving Emissions regulations. Due to the shorter, and consequently more probable, prediction horizon and the demonstrated emission improvements, a dedicated rule-based algorithm for the air path control is developed and benchmarked in the Worldwide harmonized Light-duty Test Cycle as described in the second part of this article. Worldwide harmonized Light-duty Test Cycle test results show an improvement potential for engine-out soot and [Formula: see text] emissions of up to 5.2% and 1.2%, respectively, for the air path case and a reduction of the average fuel consumption in Real Driving Emissions of up to 1% for the lean NOx trap case. In addition, the developed rule-based algorithm allows the adjustment of the desired NOx–soot trade-off, while keeping the fuel consumption constant. The study concludes with brief recommendations for future research directions, as for example, the introduction of a prediction module for the estimation of the vehicle operational profile in the prediction horizon.