Three-way catalyst modeling and fuel switch optimization of a natural gas bi-fuel-powered vehicle. (1st June 2023)

• Record Type: Journal Article
• Authors: Mahdi Teymoori, Mohammad; Chitsaz, Iman; Zarei, Atefeh
• Is Part Of: Fuel. Volume 341(2023)
• Publication Date: 2023-06-01
• Subjects: Bi-fuel engine -- Three-way catalyst -- Cold-start -- Tailpipe emissions -- Genetic algorithm, gasoline/CNG fuel switch; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.126979
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: This study simulates the three-way catalytic converter of a bi-fuel engine when operating on gasoline or compressed natural gas. To validate the simulation results with experiments, a genetic algorithm is applied to optimize the reaction mechanisms in GT-Suite software. validated model is used to optimize the vehicle's tailpipe emissions when running on CNG. NOx emissions are reduced by 15% at the end of the Optimization. THC is reduced by three percent through optimization, while CO and NOX almost remain unchanged. Abstract: In contemporary societies, improving engine efficiency and emission are critical targets for engine designers due to the increasing overwhelming popularity of electric powertrains. Compressed natural gas (CNG) is one of the alternatives for traditional fuels. Due to considerable HC emission during vehicle's cold-start, the vehicle is started with gasoline and is switched to CNG fuel after a while. The optimum fuel switching time and its effect on total tailpipe emissions have not been addressed in the literature. Therefore, this study focuses on the detailed kinetic modeling of a three-way catalytic converter of a bi-fuel (gasoline/CNG) powered vehicle. The simulated results are validated with experimental data of the new European driving cycle (NEDC) emissions test. The pre-exponential factor (A), the energy of activation (E), and the exponent of temperature (β) for the 22 reactions of the three-way catalytic converter are calibrated by optimization. A genetic algorithm is implemented to optimize the reaction coefficients in both CNG and gasoline fuel mode. The validated model is also used to optimize the vehicle's tailpipe emissions in CNG operation conditions. The results reveal that NOx catalyst conversion efficiency is acceptable in highway driving conditions for CNG mode, while the CO conversion efficiency is inappropriate under the same conditions. It is found that THC catalyst efficiency in gasoline is better than CNG mode after cold-start due to the low reactivity of methane. Most gasoline tailpipe emissions are emitted during the cold-start, while CNG tailpipe emissions gradually increase after the cold-start. The fuel switch time from gasoline to CNG is optimized to 48 s after the vehicle start. While the NOx emission decreases by 15% at the end of the driving cycle, THC is not changed after optimization. The only THC reduction optimization goal leads to a switch time increase to 149 s after the start of driving.