Internal combustion engines, used for light duty transportation, represent a major role in mobility, contributing 28.6% to CO$_2$ emissions worldwide. To mitigate environmental impact and ease the transition to clean technologies, the search for more efficient, less polluting engines has been demanded, and unique tools are necessary to meet the constantly upgraded policies. Hence, data-driven approaches that emulate current vehicles represent a valuable contribution to the improvement of engine performance. Dynamometer tests of commercial engines are open-data, and a dependable source for understanding on-road behavior of several vehicle variables.
Artificial neural network (ANN) algorithms, a subset of machine learning, have received considerable attention recently given their wide number of applications and the possibility to provide accurate data-driven approximations.
This work describes a methodology for applying ANN’s to predict emissions, efficiency, and fuel consumption in combustion engines using dynamometer test data, and to extrapolate its use in new technologies. The procedure is also applied to a hybrid vehicle case study.
The proposed methodology accurately generates ANN’s for the prediction of brake thermal efficiency (BTE), brake specific fuel consumption (BSFC) and emissions in conventional engines with 𝑅$^2$>0.91 and mean absolute errors (MAE) of less than five percent. Using the same approach, the hybrid vehicle state of charge (SOC), and the fuel scale state, are predicted, showing good agreement 𝑅$^2$>0.96 and confirming the versatility of the proposed algorithm.
Finally, an initial approach for dealing with missing data in the databases is introduced. Using various simple and iterative imputation methods, it was possible to obtain 𝑅$^2$>0.80 for predicting the BTE and BSFC with five percent of the data missing from the input values.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/676299 |
| Date | 23 March 2022 |
| Creators | Giraldo Delgado, Juan Camilo |
| Contributors | Sarathy, Mani, Physical Science and Engineering (PSE) Division, Turner, James W. G., Castaño, Pedro |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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