Computationally efficient modeling of internal combustion engine for system-level studies of vehicle power trains
Laurén, Mika (2025-10-31)
Väitöskirja
31.10.2025
Lappeenranta-Lahti University of Technology LUT
Acta Universitatis Lappeenrantaensis
School of Energy Systems
School of Energy Systems, Konetekniikka
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In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Lappeenranta-Lahti University of Technology LUT's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_ standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-412-300-6
https://urn.fi/URN:ISBN:978-952-412-300-6
Kuvaus
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Tiivistelmä
The power train development of vehicles, non-road mobile machines (NRMM), and heavyduty machines focuses on hybrid electric power trains and combustion engine technology for new, environmentally friendly fuels. Concurrently, development needs are increasing, and research and development time is shortening. Numerical simulation methods offer significant benefits for machine design, and their use is growing in mobile machine design tasks. The share of battery electric power trains and hydrogen and fuel cell-powered machines is increasing. However, it seems that combustion engines will be needed for decades in different applications where charging possibilities are limited or the operation time of electric or hydrogen-based power trains is limited.
This study aims to improve the simulation models of combustion engines for initial-phase power train studies. Three computationally efficient methods for simulating combustion engines have been developed and presented. The first method presents a static fuel consumption map estimation method from a specific fuel consumption curve, and the second method improved it by adding the dynamic torque response of the turbocharged engine, utilizing different transfer functions and time series-based models. The presented methods were validated by comparing the simulated results to the actual engine experiments. The third method utilized a reduced engine model, which was calibrated to produce reasonably accurate results of a real engine. Two of these research studies used a statistical approach to predicting the exhaust emission and fuel injection profiles.
Key findings indicate that computationally efficient and easily calibrated models can predict modern NRMM combustion engine fuel consumption and dynamic behavior. This study also expands the static fuel consumption estimation to different electric-hybrid NRMM applications and duty-cycle simulation for good accuracy. Estimating emissions and fuel injection profiles using machine learning methods shows that engine output can be predicted well; however, the accuracy of the emission formation and fuel injection profiles includes uncertainties, and further research is needed.
This study aims to improve the simulation models of combustion engines for initial-phase power train studies. Three computationally efficient methods for simulating combustion engines have been developed and presented. The first method presents a static fuel consumption map estimation method from a specific fuel consumption curve, and the second method improved it by adding the dynamic torque response of the turbocharged engine, utilizing different transfer functions and time series-based models. The presented methods were validated by comparing the simulated results to the actual engine experiments. The third method utilized a reduced engine model, which was calibrated to produce reasonably accurate results of a real engine. Two of these research studies used a statistical approach to predicting the exhaust emission and fuel injection profiles.
Key findings indicate that computationally efficient and easily calibrated models can predict modern NRMM combustion engine fuel consumption and dynamic behavior. This study also expands the static fuel consumption estimation to different electric-hybrid NRMM applications and duty-cycle simulation for good accuracy. Estimating emissions and fuel injection profiles using machine learning methods shows that engine output can be predicted well; however, the accuracy of the emission formation and fuel injection profiles includes uncertainties, and further research is needed.
Kokoelmat
- Väitöskirjat [1214]