Integration of intermittent measurement from in-cylinder pressure resonance in a multi-sensor mass flow estimator. (15th September 2019)

• Record Type: Journal Article
• Authors: Guardiola, C.; Pla, B.; Bares, P.; Peyton Jones, J.C.
• Is Part Of: Mechanical systems and signal processing. Volume 131(2019)
• Page Start: 152
• Page End: 165
• Publication Date: 2019-09-15
• Subjects: Resonance -- Internal combustion engines -- Signal processing -- Observer -- Kalman filter; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.05.052
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A method for trapped mass estimation from in-cylinder pressure resonance is used. An iterative algorithm is developed for reducing the computational burden. An outlier rejection procedure is presented for improving the robustness. An observer is designed to improve the estimation in a multi-sensor scenario. Abstract: A novel technique of trapped mass determination, based on the in-cylinder pressure resonance, has been recently published by the authors. However, the method only works when sufficient resonance intensity exists and the current formulation might preclude its implementation in real-time due to excessive computational burden. The present paper proposes an iterative algorithm for reducing the number of operations, an adaptive filter to identify faulty measurements and a Kalman filter that combines several sensors and models, currently used in commercial light-duty engines, to ensure a continous estimation of trapped mass, air mass, and exhaust gas recirculation (EGR). The filter is implemented using experimental data of a EURO 6 light-duty engine in a world harmonize light-duty test cycle (WLTC), showing the potential of being implemented in real driving conditions with robustness and harnessing a new measurement to improve the accuracy and response of current estimations.