Multidimensional Data-Driven Modelling of Engine Test Cell Data
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2021
Författare
Andersson, Helena
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In the journey towards a more sustainable vehicle fleet, requirements for lower emissions
and improved energy efficiency in gasoline engines lead to more components
being added to the internal combustion engines. This adds to the degrees of freedom
when trying to model air flow in the engine using volumetric efficiency. This
paper presents a way of modelling volumetric efficiency from engine test cell data
provided by T-Engineering – a company that designs and develops control systems
for vehicles. The model uses Gaussian process regression (GPR) for inter- and extrapolation,
including noise reduction of the measurement data. Furthermore, a
local interpretable model-agnostic explainer (LIME) is used to find regions of uncertainty
by explaining what features contribute to increasing the variance of the
GPR predictions. In addition, a neural network model is implemented in order to
improve the prediction runtime, with the purpose of enabling real-time predictions
in the control systems.
The model(s) were found to give a more physically accurate description of volumetric
efficiency than the one currently used at T-Engineering. The runtime for making
predictions for 50 data points with the neural network was ~ 0.14 ms on an AMD
Ryzen 7 PRO 4750U with Radeon Graphics 1.70 GHz and 32.0GB RAM. It remains
to investigate what the runtime on a limited CPU in the control systems will be.
Beskrivning
Ämne/nyckelord
Gaussian process regression, LIME, neural networks, volumetric efficiency, test-cell data, multidimensional modelling