Balazadeh Navid, Munshi Sandeep, Shahbakhti Mahdi, McTaggart-Cowan Gordon
School of Sustainable Energy Engineering, Simon Fraser University, Surrey, BC, Canada.
Westport Fuel Systems Inc, Vancouver, BC, Canada.
Int J Engine Res. 2025 Jul;26(7):1070-1087. doi: 10.1177/14680874241305732. Epub 2024 Dec 29.
Decarbonizing long-haul goods transportation poses a substantial challenge. High-efficiency natural gas (NG) engines, which retain the efficiency of a diesel engine but reduce the carbon content of the fuel, offer substantial potential for near-term greenhouse gas (GHG) reductions. A fast-running model that can predict engine performance, GHG and air pollutant emissions is critical to assessing this approach for different applications and vehicle drivetrain configurations. This paper presents the development, validation and application of an engine system model that adapts GT-SUITE™'s phenomenological DI-Pulse predictive model to predict the performance and emissions of a 6-cylinder NG engine using a high pressure direct-injection combustion process. The model includes the engine air exchange system, enabling the prediction of the engine and in-cylinder conditions and overall performance over transient drive cycles. The engine model with a fixed set of calibration parameters captures the complex high-pressure direct injection combustion process and generates time-resolved parameters that are fed into a coupled machine learning model to predict emissions, including nitrogen oxide (NOx) and methane (CH) emissions. While the 1-D model's predictions for CH were not accurate, coupling the 1-D engine model with a machine learning model has been shown to substantially improve the estimation of CH emissions and allow accurate prediction of engine total GHG emissions over different duty cycles. The model has been validated using transient engine dynamometer data and is then applied to assess performance and emissions over several regulatory and real-world long-haul drive cycles. The model showed an average error of less than 5% in steady operation. Cumulative errors of NOx and CH emissions in studied cycles were also less than 10%. The results showed that CH share in total GHG emissions ranges from 0.2% to 1.4% over various drive cycles. By predicting engine performance and emissions, the developed combined model has considerable potential for use in engine evaluation studies, especially when combined with new technologies across different duty cycles.
使长途货物运输脱碳面临重大挑战。高效天然气（NG）发动机保留了柴油发动机的效率，但降低了燃料的碳含量，在近期减少温室气体（GHG）排放方面具有巨大潜力。一个能够预测发动机性能、温室气体和空气污染物排放的快速运行模型对于评估这种方法在不同应用和车辆动力传动系统配置中的适用性至关重要。本文介绍了一种发动机系统模型的开发、验证和应用，该模型采用GT-SUITE™的现象学直喷脉冲预测模型，以预测采用高压直喷燃烧过程的6缸天然气发动机的性能和排放。该模型包括发动机换气系统，能够预测发动机和缸内状况以及瞬态驾驶循环中的整体性能。具有固定校准参数集的发动机模型捕捉复杂的高压直喷燃烧过程，并生成时间分辨参数，这些参数被输入到一个耦合的机器学习模型中以预测排放，包括氮氧化物（NOx）和甲烷（CH）排放。虽然一维模型对CH的预测不准确，但将一维发动机模型与机器学习模型相结合已被证明能显著改善CH排放的估计，并能准确预测不同工况下发动机的总温室气体排放。该模型已使用瞬态发动机测功机数据进行了验证，然后应用于评估几个法规和实际长途驾驶循环中的性能和排放。该模型在稳定运行时的平均误差小于5%。在所研究的循环中，NOx和CH排放的累积误差也小于10%。结果表明，在各种驾驶循环中，CH在总温室气体排放中的占比范围为0.2%至1.4%。通过预测发动机性能和排放，所开发的组合模型在发动机评估研究中具有相当大的应用潜力，特别是当与不同工况下的新技术相结合时。
