Efficiency Optimized Engine Operation with CO ₂ Neutral Fuels through Thermodynamic Loss Calculation and Model-Based Fuel Detection

Global efforts to reduce anthropogenic carbon dioxide (CO₂) emissions require innovative measures in the field of vehicle drives to present solutions in all areas of the transportation sector in the future. Synthetic fuels, that can be used in conventional combustion engines, show promising potentials. An increasing amount of synthetic fuels will be found in the off-highway sector, which is characterized by a high power and work density. The properties of synthetic fuels can differ depending on their chemical structure. In particular, the calorific value (LHV) and the stoichiometric air-fuel-ratio (AFR_st) have a direct influence on the performance and emission characteristics of an engine. In addition to providing optimal fuel-specific engine operation, fuel detection can ensure that the engine is only operated with regenerative energy carriers in future. In this paper, the methodical approach for optimizing fuel-specific engine operation on the basis of thermodynamic loss calculation and model-based fuel detection is presented using the example of the synthetic fuel oxymethylene ether (OME). In this context, quantities of the engine control unit (ECU) represent the input values of the fuel detection system. Based on this, neural networks are built to detect the regenerative share in the fuel. By calculating the thermodynamic losses, the fuel-specific losses can be quantified to derive optimization potentials. These are evaluated using steady-state operating points and cycles. The combination of fuel detection, loss quantification and optimization enables the flex-fuel operation of series engines for an optimal use of CO₂-neutral fuels.