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

Abstract

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.