Effects of Differential Pressure Sensor Gauge-Lines and Measurement Accuracy on Low Pressure EGR Estimation Error in SI Engines 2017-01-0531
Low Pressure (LP) Exhaust Gas Recirculation (EGR) promises fuel economy benefits at high loads in turbocharged SI engines as it allows better combustion phasing and reduces the need for fuel enrichment. Precise estimation and control of in-cylinder EGR concentration is crucial to avoiding misfire. Unfortunately, EGR flow rate estimation using an orifice model based on the EGR valve ΔP measurement can be challenging given pressure pulsations, flow reversal and the inherently low pressure differentials across the EGR valve. Using a GT-Power model of a 1.6 L GDI turbocharged engine with LP-EGR, this study investigates the effects of the ΔP sensor gauge-line lengths and measurement noise on LP-EGR estimation accuracy. Gauge-lines can be necessary to protect the ΔP sensor from high exhaust temperatures, but unfortunately can produce acoustic resonance and distort the ΔP signal measured by the sensor. With 30 cm gauge-lines, the lower bound on EGR valve ΔP required to maintain the EGR estimation error within ±1% increases from 4 to 10 kPa which is detrimental to engine efficiency. This paper proposes an extension of a lumped parameter model to correct for the gauge-line distortion of the ΔP signal. This correction lowers the ΔP bound back to 4 kPa. Low pass filtering is required before the differentiation of the noisy ΔP signal within the lumped parameter modeling. Filtering with the appropriate cut-off frequency maintains the ΔP lower bound despite the gauge-lines. Furthermore, a ΔP sensor with the appropriate response mimics the flow inertial lag, and further reduces the ΔP bound to 1, 1.7 and 3 kPa for ΔP sensor accuracies of ±0.1, ±0.25 and ±0.5 kPa respectively.
Citation: Kiwan, R., Stefanopoulou, A., Martz, J., Surnilla, G. et al., "Effects of Differential Pressure Sensor Gauge-Lines and Measurement Accuracy on Low Pressure EGR Estimation Error in SI Engines," SAE Technical Paper 2017-01-0531, 2017, https://doi.org/10.4271/2017-01-0531. Download Citation
Rani Kiwan, Anna Stefanopoulou, Jason Martz, Gopichandra Surnilla, Imtiaz Ali, Daniel Styles
University of Michigan, Ford Motor Company