The effect of optimizing engine control on fuel consumption and roll amplitude in ocean-going vessels: An experimental study
2015 (English)Report (Other academic)
We use data-generated models based on data from experiments of an ocean-going vessel to study the effect of optimizing fuel consumption. The optimization is an add-on module to the existing diesel-engine fuel-injection control built by Q-TAGG R&D AB. The work is mainly a validation of knowledge-based models based on a priori knowledge from physics. The results from a simulation-based analysis of the predictive models built on data agree with the results based on knowledge-based models in a companion study. This indicates that the optimization algorithm saves fuel. We also address specific problems of adapting data to existing machine learning methods. It turns out that we can simplify the problem by ignoring the auto-correlative effects in the time series by employing low-pass filters and resampling techniques. Thereby we can use mature and robust classification techniques with less requirements on the data to demonstrate that fuel is saved compared to the full-fledged time series analysis techniques which are harder to use. The trade-off is the accuracy of the result, that is, it is hard to tell exactly how much fuel is saved. In essence, however, this process can be automated due to its simplicity.
Place, publisher, year, edition, pages
Skövde, 2015. , 74 p.
Signal Processing Embedded Systems Computer Science Probability Theory and Statistics
Research subject Technology
IdentifiersURN: urn:nbn:se:his:diva-10942OAI: oai:DiVA.org:his-10942DiVA: diva2:812353
Projects“System för bränslebesparing på stora fartyg”, 2013-00301, Vinnova Forska & Väx 2013