Integration of intermittent measurement from in-cylinder pressure resonance in a multi-sensor mass flow estimator

[EN] 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 e...

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Detalles Bibliográficos
Autores: Guardiola, Carlos|||0000-0002-3150-8566, Pla Moreno, Benjamín|||0000-0001-9238-2939, Bares-Moreno, Pau|||0000-0001-9672-0819, Peyton Jones, J.C.
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/159524
Acceso en línea:https://riunet.upv.es/handle/10251/159524
Access Level:acceso abierto
Palabra clave:Resonance
Internal combustion engines
Signal processing
Observer
Kalman filter
MAQUINAS Y MOTORES TERMICOS
INGENIERIA AEROESPACIAL
Descripción
Sumario:[EN] 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.