机器学习驱动天然气掺氢发动机可预测燃烧模型的开发与验证

李弘志; 胡振宇; 陶伟俊; 黄勇成

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机器学习驱动天然气掺氢发动机可预测燃烧模型的开发与验证

更新时间：2025-11-18

- 机器学习驱动天然气掺氢发动机可预测燃烧模型的开发与验证
- Development and validation of a machine learning-driven predictive combustion model for a hydrogen-enriched compressed natural gas engine
- 默认刊物名称 2025年
- 作者机构：
  
  1.西安交通大学能源与动力工程学院,陕西省西安市710049
  2.比亚迪汽车工业有限公司,广东省深圳市518118
- 作者简介：
- 基金信息：
- DOI：1
  中图分类号： TK432
- 收稿：2025-04-22，
  
  修回：2025-07-03，
  
  录用：2025-07-05，
- 稿件说明：
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李弘志, 胡振宇, 陶伟俊, 等. 机器学习驱动天然气掺氢发动机可预测燃烧模型的开发与验证[J/OL]. 默认刊物名称, 2025.

LI Hongzhi, HU Zhenyu, TAO Weijun, et al. Development and validation of a machine learning-driven predictive combustion model for a hydrogen-enriched compressed natural gas engine[J/OL]. Moren Journal, 2025.
李弘志, 胡振宇, 陶伟俊, 等. 机器学习驱动天然气掺氢发动机可预测燃烧模型的开发与验证[J/OL]. 默认刊物名称, 2025. DOI： 1.

LI Hongzhi, HU Zhenyu, TAO Weijun, et al. Development and validation of a machine learning-driven predictive combustion model for a hydrogen-enriched compressed natural gas engine[J/OL]. Moren Journal, 2025. DOI： 1.

摘要

针对一维性能模拟软件中缺少天然气掺氢（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，本研究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，然后根据试验数据对新开发的可预测燃烧模型进行标定和验证，最后在外特性上，利用所开发的模型研究了掺氢比对发动机性能的影响。结果表明：经训练和验证后的层流燃烧速度和着火延迟时间计算模型相关系数超过0.99；利用标定后模型计算得到的发动机性能参数的误差小于3%，燃烧相位角和爆震限制点火提前角的误差小于2 °CA；受爆震和涡前排温的限制，最大允许掺氢比为20%，且随着掺氢比的增大，最佳点火提前角逐渐推迟，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）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 one-dimensional performance simulation software

and to accurately simulate the effects of HCNG’s special physical and chemical properties on engine performance

a novel machine learning-driven predictive combustion model was developed in this paper. Firstly

a machine learning approach based on artificial neural networks (ANN) was employed to develop computational models for the laminar burning velocity and ignition delay time of HCNG. Then

the newly developed combustion model was calibrated and validated against experimental data. Finally

the validated model was applied to investigate the effects of hydrogen blending ratio on engine performance under full-load conditions. The results show that the developed laminar burning velocity and ignition delay time models achieved correlation coefficients exceeding 0.99 after training and validation. Errors in predicted engine performance parameters using the calibrated combustion model were less than 3%

while errors in predicted combustion phasing angle and knock-limited spark advance angle were within 2 °CA. The maximum allowable hydrogen blending ratio is limited to 20% due to knock and pre-turbine exhaust temperature constraints. As the hydrogen blending ratio increases

the optimal spark timing gradually retards

leading to a decrease in effective thermal efficiency. Additionally

hydrogen blending accelerates combustion but increases knock propensity

while exhaust gas recirculation (EGR) slows combustion but effectively suppresses knock and advances combustion phasing. Synergistic control of hydrogen blending and EGR can enhance the engine’s effective thermal efficiency.

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