Spindt-Based AFR Sensitivities to Exhaust Emissions Measurement Accuracy for GDI Internal Combustion Engines

2021-01-0613

04/06/2021

Authors Abstract
Content
Air-fuel ratio (AFR) is one of the factors which most influences performances and emissions in internal combustion engine (ICE). Since the chemical content of exhaust gases are directly linked to the composition of fuel and air introduced in the engine, engineers have always been looking for correlations that could evaluate the AFR for each engine operating condition based on exhaust gas emissions. These relations allow to determine the effective AFR at which the engine is running. In the first part of this paper, the main correlations available in literature are reviewed pointing out their hypothesis and assumptions. Then the base Spindt formula and two of its generalizations are applied to the experimental data obtained from a gasoline direct injection supercharged engine. Subsequently the contribution added on the AFR computation by each exhaust compound has been evaluated in order to understand its weights on the final AFR value. Finally the most interesting AFR formulation has been differentiated with respect to the different compounds: this allow to evaluate the sensitivity that each correlation shows towards each compound. So, by doing this, it was possible to find out which are the species whose changes more influences the computed AFR value and so which are the ones that need to be measured with greater accuracy by the gas analyzers in the whole engine operating range. All this analysis has been carried out over the experimental data referred to a wide range of operating conditions for the engine tested.
Meta TagsDetails
DOI
https://doi.org/10.4271/2021-01-0613
Pages
15
Citation
Benvenuti, T., Maffioletti, M., and Guerrini, G., "Spindt-Based AFR Sensitivities to Exhaust Emissions Measurement Accuracy for GDI Internal Combustion Engines," SAE Technical Paper 2021-01-0613, 2021, https://doi.org/10.4271/2021-01-0613.
Additional Details
Publisher
Published
Apr 6, 2021
Product Code
2021-01-0613
Content Type
Technical Paper
Language
English