Parameteridentifikation mit estimationstheoretischen Methoden am Beispiel der dynamischen Gemischbildung eines Ottomotors

The need for dynamic models with physical system knowledge is steadily increasing with the development of new control concepts of combustion engines. The identification of the unknown parameters results in a not insignificant additional workload for the development engineer. Therefore, the automation of the identification supports the development engineer and optimizes the development process.
The goal of this thesis is to provide a complete presentation of estimation methods for the parameter identification of linear and nonlinear models and to show the power of an automated parameter identification procedure.
Pure parameter estimation methods and combined parameter and state estimation methods are examined with the example of the dynamic air and fuel ratio of a gasoline combustion engine. The parameter estimation methods tested are the Recursive Least Square, the Maximum Likelihood and the Kalman Filter. The Extended Kalman Filter and an adapted Kalman Filter with an additional Maximum Likelihood approach are used for the simultaneous parameter and state estimation methods.