基于机器学习和道路驾驶数据的预测性车辆废气挥发性有机化合物 (VOC) 排放模型的开发
Yiheng Liu1, Menglei Wang1, Zibing Yuan1
1School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
Journal of environmental sciences (China)
|December 29, 2025
概括
本研究引入了一种机器学习模型,使用发动机操作数据预测车辆挥发性有机化合物 (VOC) 排放. 该模型准确地估计了VOC,为复杂的测量仪器提供了方便的替代方案.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 计算机科学 计算机科学
背景情况:
- 汽车废气是挥发性有机化合物 (VOC) 的主要来源.
- 现有的排放模型往往忽视了发动机运行条件的变化,导致了大量的估计不确定性.
- 准确的VOC量化对于了解贡献和制定控制策略至关重要.
研究的目的:
- 开发和验证用于预测车辆VOC排放的机器学习模型.
- 确定影响VOC排放的关键车辆操作参数.
- 通过车载诊断 (OBD) 数据提供一种更方便的方法来量化车辆的VOC.
主要方法:
- 开发了一个多层感知器 (MLP) 机器学习模型.
- 该模型使用实时的VOC排放率和六个车辆操作参数 (速度,加速度,RPM,火花前进,快门位置,进气温度) 进行训练.
- 使用离子分子反应质谱仪 (IMR-MS) 和OBD系统收集数据.
主要成果:
- MLP模型表现出令人满意的预测性能,不同车辆的R平方值介于0.57和0.75之间.
- 绝对快门位置 (R=0.9) 和每分钟发动机转速 (R=0.64) 被确定为最有影响力的参数.
- 在高排放值分布较广的车辆中观察到更高的预测误差 (RMSE>45).
结论:
- 机器学习与OBD数据相结合,提供了一种可行且方便的方法来描述车辆的VOC排放.
- 这种方法通过结合实时操作条件来克服传统模型的局限性.
- 该研究开创了机器学习和OBD参数用于VOC排放特征的使用.
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