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Health Monitoring of Aging Prone Powertrain Air and Fuel Path Uncertainties: EGR Valve Area, Mass Air-Flow Sensor Bias, and Injection Drift

机译:老化易于动力总成空气和燃油路径不确定性的健康监测:EGR阀面积,质量空气流量传感器偏置和喷射漂移

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Model-based airpath control systems rely on accurate Exhaust Gas Recirculation (EGR) valve flow models and unbiased sensor/actuator measurements. Due to component tolerance ranges, sooting/clogging, and sensor aging phenomena there is a need for online estimation of adaption parameters to ensure consistent engine-out emissions are achieved vehicle-to-vehicle. This paper addresses a joint estimation problem for the Factor of Effective Area (FEA) of High-Pressure (HP) and Low-Pressure (LP) EGR valves, the estimation of Mass Air-Flow (MAF) sensor bias as well as the estimation of injector’s drift. The adaptation is based on widely-accepted discrete-time Extended Kalman Filter (EKF) that combines the intake manifold pressure model with the measurement model of the total EGR flow (computed as a difference of engine charge flow model and biased MAF sensor) further combined with the MAF sensor and intake manifold pressure sensor measurement models. It is demonstrated for diesel engine equipped with dual HP/LP EGR valves that MAF sensor bias of a magnitude ±5% and injection drift of a magnitude ±1.5mg/st can be successfully identified together with the FEA parameters. WLTC data taken from an engine dynamometer show the emissions can be maintained at the same level for an engine with simulated offsets when compared to a nominal condition.
机译:基于模型的航空控制系统依赖于精确的排气再循环(EGR)阀门流模型和非偏见的传感器/执行器测量。由于部件公差范围,烟灰/堵塞和传感器老化现象需要在线估计适应参数,以确保实现一致的发动机排放的车辆到车辆。本文针对高压(HP)和低压(LP)EGR阀的有效面积(FEA)系数的联合估计问题,估计质量气流(MAF)传感器偏置以及估计注射器的漂移。自适应基于广泛接受的离散时间扩展卡尔曼滤波器(EKF),其将进气歧管压力模型与总EGR流的测量模型相结合(计算为发动机电荷流模型和偏置MAF传感器的差异)进一步组合使用MAF传感器和进气歧管压力传感器测量模型。对于配备有双HP / LP EGR阀的柴油发动机证明了MAF传感器偏置的±5%和注射漂移幅度±1.5mg / st,可以与FEA参数一起识别。从发动机测功机取出的WLTC数据显示,与标称条件相比,可以在具有模拟偏移的发动机的发动机处保持排放量。

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