Deterministic and stochastic models of automotive engine fuelled with palm biodiesel

Abstract

In this paper, two types of dynamic model for automotive diesel engine fuelled with palm biodiesel (Palm Oil Methyl Ester) are being developed using a black box technique. Assuming a discrete time form for the system model, an Autoregressive with eXogenous input (ARX) and an Autoregressive Moving Average with eXogenous input (ARMAX) model structures were selected in this work. The models are derived for the development of real-time self-tuning speed controller purposes. Real-time input-output data obtained from a 2.0L automotive diesel engine with test-bed unit were used for modeling. A Pseudo-Random Binary Sequence (PRBS) with maximum lengths sequence of 31 has been used as the input signal to determine the dynamic models of automotive palm biodiesel engine for medium speed range around 2100 rpm. The input and output signals were interfaced to the plant via Matlab programming. Recursive estimation algorithms, Recursive Least Squares (RLS) and Recursive Extended Least Squares (RELS), were used to estimate the parameters of each model. Finally, model validation was done by plotting the output predicted by the model and comparing it with the measured output.