Automatic detection of the fuel composition in a Diesel Engine : Identifying fuel composition in the fuel system of a combustion engine and optimising for computational complexity

Sammanfattning: The transportation industry is responsible for 26% of all emission of greenhouse gases in the European Union. Many steps are being taken to minimise greenhouse gas emissions. The most effective way to reduce the emission of greenhouse gases is by transitioning to biofuels. The combustion engines in most vehicles perform below their potential efficiency when running on biofuels due to the reduced energy density. The characteristics of the injection into the combustion chamber can be adjusted if the fuel type being injected is known. In Diesel engines, Fatty Acid Methyl Esters (FAME) is one of the most used biofuels. The higher weight density and lower energy density of FAME compared to Diesel result in lower power output when used in a Diesel engine. Detecting the fuel composition in the engine would allow for adaptation to the injection characteristics and bring back the engine’s efficiency to its full potential independent of the fuel composition. The most significant issue with fuel composition prediction is that no work has been done in this field using machine learning. There are several hundreds of features inside the control system of a truck. The selection of which features contribute to the prediction of fuel composition is important and challenging. The prediction should be computationally inexpensive and relatively accurate to facilitate in-time prediction. Using a feature selection method based on Shapley additive explanations (SHAP) applied to an expert network enables feature selection perfectly tailored for finding the optimal features that combined will provide accurate predictions with minimal computational resources. This feature selection method has been tested before but with limited analysis and adaptation. We apply various feature selection methods and propose a new feature selection method coined SHAP-C, which outperforms all other feature selection methods we have tested for this particular scope of application. The results show that with a minimal network of two input features and six hidden nodes, the fuel composition can be predicted with a 98.82% accuracy using a total of 75 floating-point operations. The low computational complexity allows for real-time predictions in the control system of a truck, which can be used to modulate the injection characteristics into the engine’s combustion chamber. The network used to identify the fuel composition has been trained with data from a single truck. The results are therefore not generalised across trucks. This adjustment based on fuel composition would allow a truck to run optimally independent of the fuel composition.