As the road transport accounts between 15%–18% of worldwide CO2 emissions, the automotive sector has a deep commitment to mitigate global warming. Consequently, stricter regulations have been adopted by the European Union and worldwide to reduce that big impact. Approximately, 10% of the energy generated by fuel combustion in the engine is destined to the auxiliaries components activation and the movement of mechanical elements with relative motion between themselves. A reduction on that figure or alternatively a mechanical efficiency improvement can be directly translated on target alignment. The aim of this work is developing a model to predict the mechanical and friction losses and its distribution in a four-stroke direct injection-diesel engine and simulating different strategies, which increment the engine efficiency. A 1D model has been developed and fitted in gt-suite based on the experimental results of a 1.6-L diesel engine. Additionally, a description of the tribological performance has been realized in different parts of the engine where friction is present. Finally, the engine friction maps have been broken down in order to quantify the friction losses produced in the piston ring assembly, crankshaft bearings, and valvetrain.
Transient car emission tests generate huge amount of test data, but their results are usually evaluated only using their “accumulated” cycle values according to the homologation limits. In this work, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Different from the conventional approach, the engine parameters and fuel consumption were acquired from the Engine Control Unit, not from the test measurement equipment. Instantaneous engine values were used as input in machine-learning-based digital twins. This novel approach allows for much less costly vehicle tests and optimizations. The paper’s novel approach and developed digital twins model were able to predict both instantaneous and accumulated fuel consumption with good accuracy, and also for tests cycles different to the one used to train the model.
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