Predictive Combustion Model for Hydrogen-Enriched Compressed Natural Gas Engines Using Machine Learning

LI Hongzhi; HU Zhenyu; TAO Weijun; HUANG Yongcheng

doi:10.7652/xjtuxb202601015

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Predictive Combustion Model for Hydrogen-Enriched Compressed Natural Gas Engines Using Machine Learning

Special Topic Pollution Control and Resource Utilization in the “Dual Carbon”Context | 更新时间：2026-01-15

- Predictive Combustion Model for Hydrogen-Enriched Compressed Natural Gas Engines Using Machine Learning
- Journal of Xi'an Jiaotong University Vol. 60, Issue 1, Pages: 150-161(2026)
- 作者机构：
  
  西安交通大学能源与动力工程学院，710049，西安
  比亚迪汽车工业有限公司，518118，广东深圳
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202601015
  CLC： TK432
- Received：22 April 2025，
  
  Published：10 January 2026
- 稿件说明：
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LI Hongzhi, HU Zhenyu, TAO Weijun, et al. Predictive Combustion Model for Hydrogen-Enriched Compressed Natural Gas Engines Using Machine Learning[J]. Journal of Xi'an Jiaotong University, 2026, 60(1): 150-161.
DOI：

LI Hongzhi, HU Zhenyu, TAO Weijun, et al. Predictive Combustion Model for Hydrogen-Enriched Compressed Natural Gas Engines Using Machine Learning[J]. Journal of Xi'an Jiaotong University, 2026, 60(1): 150-161. DOI： 10.7652/xjtuxb202601015.

摘要

针对发动机一维性能仿真软件GT-Power中缺少天然气掺氢（HCNG）燃烧基础数据的问题，同时为准确模拟HCNG特殊物化特性对发动机性能的影响，开发了一种新的机器学习驱动的可预测燃烧模型。首先基于人工神经网络（ANN）的机器学习方法开发了HCNG的层流燃烧速度和着火延迟时间计算模型，然后对上述模型进行了误差分析与验证，并将其写成子程序与GTPower软件耦合，最后根据试验数据对新开发的模型进行标定和验证，并在外特性上利用所开发的模型研究了氢气体积分数

（H

）（掺氢比）对发动机性能的影响。结果表明：训练后的ANN燃烧模型预测的层流燃烧速度和着火延迟时间与原值之间的相关系数均超过0.99；利用标定后的模型计算得到的发动机性能参数误差小于3%，燃烧相位角和爆震限制点火提前角的误差小于2°；受爆震和涡前排温的限制，最大允许的

（H

）为20%，且随着

（H

）的增大，最佳点火角度逐渐推迟，有效热效率随之下降；掺氢可加快燃烧但会增加爆震倾向，而废气再循环（EGR）技术虽然会减缓燃烧但可有效抑制爆震、提前燃烧相位，两者协同控制可提高发动机的有效热效率。

Abstract

To address the lack of fundamental combustion data for hydrogen-enriched compressed natural gas (HCNG) in engine one-dimensional performance simulation software GT-Power，and to accurately simulate the effects of HCNG's unique physicochemical properties on engine performance，a novel machine learning-driven predictive combustion model is developed.Firstly，a computational model for the laminar burning velocity and ignition delay time of HCNG is developed based on a machine learning approach utilizing an artificial neural network (ANN). Subsequently，the model undergoes error analysis and validation，and is implemented as subroutines coupled with GT-Power software.Finally，the newly developed model is calibrated and validated against experimental data.Using this model，the influence of hydrogen volume fraction

)(hydrogen blending ratio)on engine performance is investigated under external characteristic conditions.The results indicate that：The correlation coefficients between the laminar burning velocity and ignition delay time predicted by the trained ANN combustion model and the original values b

oth exceed 0.99；the error in engine performance parameters calculated using the calibrated model is less than 3%，with errors in combustion phasing and knock-limited spark advance angle being less than 2°；limited by knock and pre-turbine exhaust temperature，the maximum allowable

)is 20%；As

)increases，the optimal spark timing is gradually delayed，and the effective thermal efficiency decreases；hydrogen enrichment accelerates combustion but increases knock tendency，while Exhaust Gas Recirculation (EGR) slows combustion but effectively suppresses knock and advanced combustion phasing，with the synergistic control of both hydrogen blending and EGR improving the engine's effective thermal efficiency.

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