Model-Based Emission Control of a Compression Ignition Engine

Zweigel, René; Abel, Dirk (Thesis advisor); Pitsch, Heinz (Thesis advisor)

Aachen (2017, 2018)
Dissertation / PhD Thesis


There is an ongoing need for investigation of clean and efficient combustion processes, within the automotive sector in particular, as combustion engines will remain an important power source for the next decades. Therefore, the development of new combustion processes and adequate combustion control strategies is required to fulfill the increasing demands regarding comfort, driveability, and eco-friendliness. Overall objectives are the reduction of pollutants without efficiency loss. In this context, low-temperature combustion (LTC) is a promising alternative to conventional diesel combustion (CDC), during part-load Operation in particular. LTC is characterized by advanced injection timings and higher amounts of recirculated exhaust gas (EGR). Hence, LTC lacks a direct trigger for the start of combustion (SOC) and tends to an incomplete and unstable combustion. Therefore, combustion control is essential. This thesis focuses on the research of sophisticated control approaches for automotive compression-ignition (CI) engines applying LTC. Challenges are the determination of relevant correlations among actuators and engine responses, the adequate modeling of these relations as a basis for model-based control methods, and the control of the complex, coupled, and non-linear combustion system. The control objective is the reduction of nitrogen oxides (NOx) in a new and direct way considering unburnt hydrocarbons (THC) emissions as well. The presented cycle-based approach allows a combustion with less pollutants and equal or higher efficiency level than CDC. Experiments and evaluations are carried out with a four-cylinder engine on a test bench. The results are presented and discussed. Finally, a further control approach is introduced, which is able to influence the combustion and the corresponding pollutant formation more directly within the combustion cycle. Therefore, a rate-shaping capable injector system is combined with a model-based iterative learning method to adjust the in-cyclevariant fuel rate. The potential of this new method is evaluated in simulations.