Use of thermodynamic models for compression ratio and peak firing pressure optimization in heavy-duty diesel engine

Abstract

European Union Commission declared 30% CO2 reduction target for 2019 report-out for heavy-duty vehicles. Nowadays, the main focus of automotive manufacturers is reducing CO2 emissions. To ensure 30% CO2 emission reduction, companies are examining the effect of different fuel economy contributors. Developing prototype diesel engines and performing tests for different conceptual studies are both expensive and complicated conditions. As a result, OEMs should employ virtual tools with an enhancing trend. In this paper, the fuel economy improvement effects of increasing the compression ratio and maximum in-cylinder pressure limitation in the 12.7L heavy-duty diesel engine are examined using a thermodynamic model rather than actual dynamometer tests. Initially, the generated thermodynamic model is correlated with dynamometer test data. A predictive combustion model is used. NOx emission prediction correlation is also performed. Then, the most vital operating points representing VECTO and on-road fuel economy cycles are detected. At these critical operating points, the thermodynamic model is run with varied calibration parameters such as fuel injection quantity & timing, boost pressure & air mass flow set points. Thermodynamic model outputs are used for the Gaussian Process Model generation. A multi-objective optimization methodology is utilized to thoroughly examine the fuel consumption and NOx emission trade-off trends for various peak firing pressure hardware limit and compression ratios scenarios. It is proven that since the fuel residency of part load points is more critical than full load points in the VECTO cycle and long haul vehicles, the CO2 reduction potential of increasing compression ratio is observed to be comparatively lower concerning increasing peak firing pressure limit. The methodology can be utilized for performing fast and accurate fuel economy benefits examination of different technologies or concepts in a virtual platform resulting in a significant reduction in the number of actual dynamometer tests.