Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM

Trigas Badmianto(1*), Eka Firmansyah(2), Adha Imam Cahyadi(3)

(1) Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada
(2) Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada
(3) Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada
(*) Corresponding Author


In four-stroke engine injection system, often called spark ignition (SI) engine, the air-fuel ratio (AFR) is taken from the measurement of lambda sensor in the exhaust. This sensor does not directly describe how much AFR in the combustion chamber due to the large transport delay. Therefore, the lambda sensor is used only as a feedback in AFR control "correction", not as the "main" control. The purpose of this research is to identify the parameters of the non-linear system in SI engines to produce AFR estimator. The AFR estimator is expected to be used as a feedback of the main "AFR" control system. The process of identifying the parameters using the Gauss-Newton method, due to its rapid computation to Achieve convergence, is based on prediction error minimization (PEM). The models of AFR estimator is an open-loop system without a universal exhaust gas oxygen (UEGO) sensors as feedback, called a virtual AFR sensor. The high price of UEGO sensors makes the virtual AFR sensor can be a practical solution to be applied in AFR control. The model in this research is based on the mean value engine models (MVEM) with some modifications. The research dataset was taken from a Hyundai Verna 2002 with the additional UEGO type of lambda sensors. The throttle opening angle (input) is played by stepping on the gas pedal and the signal to the injector (input) is set to a certain quantity to produce the AFR (output) value read by the UEGO sensor. This research produces an open loop estimator model or AFR virtual sensors with normalized root mean square error (NRMSE) = 0.06831 = 6.831%.


Parameter identification, Air-Fuel Ratio, MVEM, Spark-ignition engines, fuel injection system, Prediction error minimization

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