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Vehicle Exhaust Emission Control-Dynamic Signature Measurement and Analysis - A Method to Detect Emission Testing Irregularities

机译:车辆排气控制 - 动态签名测量和分析 - 一种检测排放测试不规则性的方法

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To meet US EPA light-duty vehicle emission standards, the vehicle powertrain has to be optimally controlled in addition to maintaining very high catalyst system efficiency. If vehicles are operated outside the bounds of a standard laboratory exhaust emission test (e.g., on-road or off-cycle) the operating control strategy may shift to optimize other desirable parameters such as fuel economy and drivability. Under these circumstances. The engine control system could be operating in a different state space from an emission control stand point. This control state-space can be observed based on four principal parameters: NO_x, Lambda and exhaust temperature (measured at the tailpipe) and vehicle acceleration. These vehicle emission control patterns can be characterized by their corresponding emission control signatures, such as cold start, transient fuel control, and high speed/high load open loop. These emission control signatures are unique to a variety of engine technologies as well. Recognizing these signatures during vehicle operation can identify engine control state space and could estimate NO_x mass flow by utilizing an ANN (artificial neural network) for pattern recognition. This could assist in detecting emission testing irregularities that might indicate a malfunctioning emission control system. One advantage to this approach is the equipment overhead to acquire this information is much less compared to other conventional methods such as PEMS (portable emission measurement system). US EPA is investigating this approach, recording the vehicle emission control dynamic signatures during normal dynamometer testing and on-road/off-cycle. Optimized data sets of emission control signatures are currently being used for training an artificial neural network to estimate NO_x mass-based calculations and distinguish between well-controlled and uncontrolled systems. This non-intrusive testing method may be used to detect catalyst early failure and monitor emission test irregularities.
机译:为了满足美国环保署的轻型汽车排放标准,车辆驱动系统具有除了得到最佳控制,以保持非常高的催化剂系统的效率。如果车辆标准实验室废气排放测试的边界之外操作时(例如,在道路上或关断周期)的操作控制策略可以转移到优化其他期望的参数,如燃油经济性和驾驶性能。在这些情况下。的发动机控制系统可以在从发射控制站立点不同的状态空间中操作。中NO_x,Lambda和排气温度(在尾管测量)和车辆加速度:可以基于四个主要参数被观察到该控制状态空间。这些车辆排放控制模式可以通过它们相应的发射控制签名,如冷启动,瞬态燃料控制,和高速/高负载的开环来表征。这些排放控制的签名是唯一的各种发动机技术,以及。在车辆运行期间认识到这些特征可以识别发动机控制状态空间,并且可以通过利用ANN(人工神经网络)用于模式识别估计NO_x的质量流量。这可以帮助检测发射检测违规行为可能表明发生故障的排放控制系统。一个优点这种方法是设备的开销来获取该信息与其它常规方法如PEMS(便携式发射测量系统)要少得多。美国环境保护署正在调查这一做法,在正常的功率测试和道路/记录车辆排放控制动态签名非周期。当前正在使用的用于训练人工神经网络来估计NO_x的基于质量的计算和良好控制的和失控的系统区分发射控制签名的优化的数据集。可以使用这种非侵入性的检测方法来检测催化剂早期失效和监控发光测试不规则性。

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