澄清重型柴油车的发动机运行参数对NOx和CO2排放的影响,使用多式联气融合方法
Tao Li1, Xin Lou1, Zhuoqian Yang2
1Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha 410000, China.
The Science of the total environment
|September 30, 2024
概括
准确的车辆排放预测对于环境保护至关重要. 使用车载诊断 (OBD) 数据和人工智能的新方法改善了重型柴油车排放预测,提高了监管效率.
科学领域:
- 环境科学 环境科学
- 汽车工程 汽车工程
- 数据科学数据科学数据科学
背景情况:
- 交通运输部门,特别是重型柴油车辆,是空气污染的重要来源.
- 中国VI排放标准要求车载诊断 (OBD) 系统提供实时数据,但数据质量不足以达到监管目的.
- 准确预测氧化物 (NOx) 和二氧化碳 (CO2) 等排放量对于环境和健康保护至关重要.
研究的目的:
- 开发一种先进的方法来预测重型柴油车辆的质量排放率.
- 为了提高从车载诊断 (OBD) 系统获得的排放数据的准确性.
- 为有效的车辆排放监管和环境保护提供一个强大的框架.
主要方法:
- 开发了一种名为OBD-PSOMFF的新方法,集成多模式特征融合与粒子群集优化 (PSO).
- 长短期内存 (LSTM) 网络被用来从OBD指示器中提取时间特征.
- 粒子集群优化 (PSO) 用于优化特征权重,以提高预测准确度.
主要成果:
- 拟议的OBD-PSOMFF方法在预测NOx和CO2质量排放率方面取得了显著的改进.
- 与基本的LSTM模型相比,NOx和CO2预测的平均平方误差分别减少了65.205%和70.936%.
- 该研究通过23辆重型车辆的数据验证了多式联动融合方法的有效性.
结论:
- 开发的多式联通融合方法为预测车辆排放提供了可靠和准确的框架.
- 这种方法提高了OBD数据的实用性,以有效监管重型柴油车辆的排放.
- 这些发现有助于通过改进排放监测和控制来推进环境保护战略.
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