Journal
VEHICLES
Volume 5, Issue 2, Pages 583-604Publisher
MDPI
DOI: 10.3390/vehicles5020032
Keywords
machine learning; fuel consumption prediction; digital twins
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In this study, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Unlike traditional methods, the engine parameters and fuel consumption were acquired from the Engine Control Unit instead of test measurement equipment. By using instantaneous engine data as input, accurate predictions were made using machine learning-based digital twins.
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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