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The increasingly stringent emissions regulations together with the demand of highly efficient vehicles from the customers, lead to rapid developments of distinct powertrain solutions, especially when the electrification is present in a certain degree. The combination of electric machines with conventional powertrains diversifies the powertrain architectures and brings the opportunity to save energy in greater extents. On the other hand, alternative combustion modes as reactivity controlled compression ignition (RCCI) have shown to provide simultaneous ultra-low NOx and soot emissions with similar or better thermal efficiency than conventional diesel combustion (CDC). In addition, it is necessary to introduce more renewable fuels as ethanol to reduce the total CO2 emitted to the atmosphere, also called well-to-wheel (WTW) emission, in the transport sector.

A genetic algorithm (GA) optimization methodology is applied to the design of the combustion system of a heavy-duty (HD) Diesel engine fueled with dimethyl ether (DME). The study has two objectives, the optimization of a conventional diffusion-controlled combustion system aiming to achieve US2010 targets and the optimization of a stoichiometric combustion system coupled with a three way catalyst (TWC) to further control NOx emissions and achieve US2030 emission standards. These optimizations include the key combustion system related hardware, bowl geometry and injection nozzle design as input factors, together with the most relevant air management and injection settings. The GA was linked to the KIVA CFD code and an automated grid generation tool to perform a single-objective optimization. The target of the optimizations is to improve net indicated efficiency (NIE) while keeping NOx emissions, peak pressure and pressure rise rate under their corresponding target levels.