Fuzzy logic control applied on SI engine concerning the injection time evolution

Marín, Daniel Jacobo; Hiticas, Ioan; Mihon, Liviu

doi:10.1109/cinti.2012.6496774

Cited by 1 publication

(1 citation statement)

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“…This classic controller uses algorithms if-then-rule, in the form of look-up table [5], [6], or fuzzy logic controller [5], [7], as feedforward control.…”

Section: Iatmentioning

confidence: 99%

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

Badmianto

Firmansyah

Cahyadi

2018

IJITEE

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Abstract-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%. (model) is required to obtain an accurate mathematical model. A good model is a model that more accurately reflects the dynamics of the system or plant. The dynamics of air-fuel ratio (AFR) on SI injection system Engine is very non-linear and multivariable [1], makes the process of the parameter identification difficult compared with the linear system and a few variables. Keywords--ParameterThe AFR is an important indicator that affects exhaust emissions, engine power, and fuel consumption in Spark Ignition (SI) Engine. The influence of lambda (normalized AFR) on power and fuel consumption are presented in Fig. 1. Maximum power will be achieved if the lambda value is in the range of 0.82 (AFR = 12:1) [2], while the fuel consumption will be achieved if the minimum number lambda is in the range of 1:09 (AFR = 16:1) [2].

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“…This classic controller uses algorithms if-then-rule, in the form of look-up table [5], [6], or fuzzy logic controller [5], [7], as feedforward control.…”

Section: Iatmentioning

confidence: 99%