汽车远程尾气检测系统的设计与应用

发布时间：2018-06-02 10:52

本文选题：发动机 + 尾气　；参考：《南京邮电大学》2017年硕士论文

【摘要】：目前,我国对尾气排放情况进行监控的设备基本采用的都是静态定点检测方式。这种检测模式不但不能保证准确分析汽车在动态运行过程中的尾气排放情况,而且其测量结果也容易受到来自人为干涉或检测环境的干扰,造成部分排放超标的车辆通过检测上路行驶,加剧对环境造成的污染。本文设计实现了一种基于SOC3210硬件平台和Linux的远程车载尾气检测系统。该系统能够对汽车尾气排放情况和发动机工况进行实时检测,并通过3G无线通信网络平台把相关数据发送给尾气监控中心。通过RBF神经网络拟合的模型对发动机尾气状况进行辨识,以便及时发现车辆的异常排放。当尾气排放超标时,能够提醒驾驶员及时进行车辆技术状况检查与维修。同时尾气监控中心也能通过3G无线通信网络及时获取车辆的排放情况,这为相关部门进行汽车尾气治理提供了原始的数据。通过在福特福克斯车辆上的实验验证,结果表明本文设计的汽车远程尾气检测系统能够实时对发动机工况和尾气数据进行远程监控,并且其对尾气异常状况检测的准确率达到了90.64%。
[Abstract]:At present, the equipment used to monitor the exhaust emission in our country is static fixed-point detection method. This kind of detection mode can not only guarantee the accurate analysis of the exhaust emissions during the dynamic operation of the vehicle, but also be easily interfered by the measurement results from the man-made interference or the detection environment. Vehicles that cause part of the emissions to exceed the standards are tested on the road, increasing pollution to the environment. This paper designs and implements a remote vehicle exhaust detection system based on SOC3210 hardware platform and Linux. The system can detect the exhaust emission and engine working conditions in real time, and send the relevant data to the exhaust monitoring center through 3G wireless communication network platform. The engine exhaust was identified by the model fitted by RBF neural network in order to detect the abnormal emission of vehicle in time. When exhaust emissions exceed the standard, drivers can be reminded to carry out vehicle technical condition inspection and maintenance in time. At the same time, the emission monitoring center can get the emission of the vehicle through 3G wireless communication network in time, which provides the original data for the relevant departments to deal with the vehicle exhaust. The experimental results on Ford Fox vehicle show that the remote exhaust detection system designed in this paper can remotely monitor engine working conditions and exhaust gas data in real time. The accuracy of detecting the abnormal state of tail gas is 90.64.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X84

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