Yang Luoshu, Ge Yunshan, Lyu Liqun, Tan Jianwei, Hao Lijun, Wang Xin, Yin Hang, Wang Junfang
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Environ Res. 2024 Apr 15;247:118190. doi: 10.1016/j.envres.2024.118190. Epub 2024 Jan 17.
Vehicle emissions have a serious impact on urban air quality and public health, so environmental authorities around the world have introduced increasingly stringent emission regulations to reduce vehicle exhaust emissions. Nowadays, PEMS (Portable Emission Measurement System) is the most widely used method to measure on-road NOx (Nitrogen Oxides) and PN (Particle Number) emissions from HDDVs (Heavy-Duty Diesel Vehicles). However, the use of PEMS requires a lot of workforce and resources, making it both costly and time-consuming. This study proposes a neural network based on a combination of GA (Genetic Algorithm) and GRU (Gated Recurrent Unit), which uses CC (Pearson Correlation Coefficient) to determine and simplify OBD (On-board Diagnosis) data. The GA-GRU model is trained under three real driving conditions of HDDVs, divided by vehicle driving parameters, and then embedded as a soft sensor in the OBD system to monitor real-time emissions of NOx and PN within the OBD system. This research addresses the existing research gap in the development of soft sensors specifically designed for NOx and PN emission monitoring. In this study, it is demonstrated that the described soft sensor has excellent R values and outperforms other conventional models. This research highlights the ability of the proposed soft sensor to eliminate outliers accurately and promptly while consistently tracking predictions throughout the vehicle's lifetime. This method is a groundbreaking update to the vehicle's OBD system, permanently adding monitoring data to the vehicle's OBD, thus fundamentally improving the vehicle's self-monitoring capabilities.
车辆排放对城市空气质量和公众健康有严重影响，因此世界各国的环境当局都出台了日益严格的排放法规，以减少车辆尾气排放。如今，便携式排放测量系统（PEMS）是测量重型柴油车（HDDV）道路氮氧化物（NOx）和颗粒物数量（PN）排放最广泛使用的方法。然而，使用PEMS需要大量人力和资源，成本高且耗时。本研究提出了一种基于遗传算法（GA）和门控循环单元（GRU）相结合的神经网络，该网络使用皮尔逊相关系数（CC）来确定并简化车载诊断（OBD）数据。GA-GRU模型在重型柴油车的三种实际驾驶条件下，根据车辆行驶参数进行训练，然后作为软传感器嵌入OBD系统，以监测OBD系统内NOx和PN的实时排放。本研究解决了专门用于NOx和PN排放监测的软传感器开发方面现有的研究空白。在本研究中，结果表明所描述的软传感器具有出色的R值，并且优于其他传统模型。本研究突出了所提出的软传感器能够准确、迅速地消除异常值，同时在车辆的整个使用期限内持续跟踪预测。该方法是对车辆OBD系统的一次突破性更新，永久性地将监测数据添加到车辆的OBD中，从而从根本上提高车辆的自我监测能力。
