TITLE:
Parameter Identifiability and Parameter Estimation of a Diesel Engine Combustion Model
AUTHORS:
Lilianne Denis-Vidal, Zohra Cherfi, Vincent Talon, El Hassane Brahmi
KEYWORDS:
Calibration, Genetic Algorithms, Global Optimization, Identifiability, Identification Algorithms, Internal Combustion Engines
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.2 No.5,
April
25,
2014
ABSTRACT: In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustion model. This model is used for design, validation and pre-tuning of engine control laws. A cascade algebro-differential elimination method is used for studying identifiability. This investigation is done by using input-output-parameter relationship. Then these relations are transformed by using iterated integration. They are combined with an original numerical derivative estimation based on distribution theory which gives explicit point-wise derivativeestimation formulas for each given order. Then new approximate relations, linking block of parameters and outputs (without derivative) are obtained. These relations are linear relatively to the blocks of parameters and yield a first estimation of parameters which is used as initial guess for a local optimization method (least square method and a local search genetic algorithm).