Fast NO_x emission prediction methodology via one-dimensional engine performance tools in heavy-duty engines

Abstract

Accurate prediction of nitrogen oxide, soot, carbon monoxide and unburned hydrocarbon emissions from diesel engines plays a crucial role during the design and development phases of vehicle powertrain systems due to increasingly more strict emission legislation. Undoubtedly, generating accurate and robust emission prediction methods will serve to global optimization of engine components at very early stages of engine development. Engine component selection, accurate specific fuel consumption prediction and defining the correct exhaust gas recirculation strategy (low and mid-high) can only be performed via accurate and fast emission prediction. There are many possible ways of emission prediction in the literature, such as three-dimensional computational fluid dynamics, stochastic reactor, semi-empirical, phenomenological models and neural networks. However, these prediction methods either need excessive test data or simulation duration. However, using one-dimensional simulation tools is a faster way of emission prediction, but has low accuracy. In this study, it is aimed to develop a fast NO x emission prediction methodology by utilizing one-dimensional models generated in GT-Suite software. Two different heavy-duty engines are modelled, and the models are correlated with test data. An NO x emission prediction methodology is developed with the 9-L heavy-duty diesel engine model. Extended Zeldovich mechanism included in the software is tuned via embedding different calibration multiplier maps. Comparison of simulation results with test data shows that turbine inlet temperature, in-cylinder maximum temperature, maximum pressure, load, CA50, exhaust gas recirculation rate and fuel-air ratio are the most critical map parameters for enhanced emission prediction capability. The methodology developed is applied to the 12.7-L engine with these selected map parameters. The results show that the methodology can be used to predict NO x values with high speed and sufficient accuracy for different heavy-duty diesel engine variants.

Prediction Model for Transient NOx Emission of Diesel Engine Based on CNN-LSTM Network

“…To accurately predict NOx emissions from diesel engines, researchers, both domestically and internationally, have proposed methods based on physical models [4,5], as well Energies 2023, 16, 5347 2 of 21 as a combination of physical models with MAP mapping [6,7]. While these prediction methods can effectively estimate NOx emissions under steady-state conditions, they face challenges in accurately predicting transient NOx emissions due to the rapid changes in diesel engine speed, torque, and fuel injection during transient conditions.…”

Virtual battery electric vehicle development via 1D tools

“…Heavy-duty diesel engine performance development studies will continue within the next three decades. There is still hope to lower the diesel engine related emissions significantly, as it is stated in different studies: O ¨zgu¨l et al, 3 Aldawood et al, 4 Smallbone et al, 5 Kim et al, 6 and Warnatz et al 7 On the other hand, it can be easily anticipated that the strict fuel consumption reduction targets cannot be fulfilled by only developing conventional technologies. OEMs are currently focusing on creating a bundle of products, including conventional technologies and advanced ones such as electrification and alternative fuels.…”