Browse Publications Technical Papers 2009-01-0590
2009-04-20

Low-Cost Air Estimation 2009-01-0590

The focus of this paper is an air charge estimator for engine control system applications which do not feature a mass air flow (MAF) sensor. The proposed approach, beyond its independency of a MAF sensor, is designed to be compatible with the confines of a typical production control system configuration. The air charge estimation algorithm is based on mean-value models for the manifold pressure dynamics and the gas flows through the throttle and valve orifices. It involves nominal static models for the volumetric efficiency of the engine and for the throttle discharge coefficient. The static models for those parameters are complemented with correction factors that are adjusted on-line. The update of the volumetric efficiency correction is implemented in the form of a Kalman-filter which uses the difference between the measured and the modeled manifold pressure as an error metric. The discharge coefficient correction, on the other hand, is implemented in the form of an adjustable correction look-up table. The adjustment of the correction table evolves as a function of the operating condition and is based on an error metric which is derived from the value of the closed-loop fuel control correction factor. The performance of the algorithm is tested on the basis of various realistic driving maneuvers, i.e., maneuvers that include the first 18 cycles of the FTP test schedule and the US06 driving schedule. The experimental tests confirm that the algorithm meets typical performance requirements and that the overall system performance is indeed even comparable to the performance of MAF sensor based systems. The proposed approach is certainly a prime candidate for deployment in markets with moderate emission standards. However, in light of its cost effectiveness and especially its adaptive capabilities, which make up for many of the long-term detriments commonly seen among MAF-sensor-less low-cost solutions, the approach may even have potential in markets with more stringent emission standards.

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