Main menu


Automotive Engines Control Estimation Statistical Detection

 Download Automotive Engines Control Estimation Statistical Detection  Easily In PDF Format For Free.


Increasing requirements for a fuel economy, exhaust emissions and the output performance and also the complexity of the automotive engines necessitate the development of a new generation of the engine control functionality. 

This book offers the solutions of a number of the engine control and estimation problems and consists of ten Chapters grouped in four Parts. Idle speed control and cylinder flow estimation techniques are presented in the first Part of the book; engine torque and friction estimation methods are presented in the second Part; engine misfire and Cam Profile Switching diagnostic methods are presented in the third Part; and engine knock detection and control algorithms are discussed in the fourth Part of the book.

 The algorithms presented in the first Part of the book use a mean value engine model and the techniques described in the rest of the book are based on the cylinder individual engine model. The book provides a sufficiently wide coverage of the engine functionality. The book also offers a tool-kit of new techniques developed by the author which was used for the problems described above.

 The techniques can be listed as follows: input estimation, composite adaptation, spline and trigonometric interpolations, a look-up table adaptation and a threshold detection adaptation. These methods can successfully be used for other engine control and estimation applications. 

These methods are listed in Table 0.1 and Table 0.2 which contain a brief description of the methods, application areas and references providing a reader with the overview and a guidance through the book.

The performance of air charge estimation algorithms in spark ignition automotive engines can be significantly enhanced using advanced estimation techniques available in the controls literature. 

This Chapter illustrates two approaches of this kind, that can improve the engine cylinder flow estimation. The first approach is based on an input observer while the second approach relies on an adaptive estimation.

 Assuming that the cylinder flow is nominally estimated via aspeed-densitycalculation, and that the uncertainty is additive to the volumetric efficiency, the straightforward application of an input observer provides an easy-to-implement algorithm that corrects the nominal air flow estimate. The experimental results presented in this Chapter point to a sufficiently good transient behavior of the estimator. 

The signal quality may be deteriorating, however, for extremely fast transients. This motivates the development of an adaptive estimator that relies mostly on the feedforward speed-density calculation during transients while during engine operation close to steady-state conditions, it relies mostly on the adaptation. In our derivation of the adaptive estimator the uncertainty is modeled as an unknown parameter multiplying the intake manifold temperature. 

The tracking error between the measured and modeled intake manifold pressure together with an appropriately definedprediction error estimateare used in the adaptation algorithm with the improved identifiability and convergence rate.

 A robustness enhancement, via aσ-modification with the σ-factor depending on the prediction error estimate, ensures that in transients the parameter estimate converges to a pre-determineda priorivalue.

 In close to steady-state conditions, theσ- modification is rendered essentially inactive and the evolution of the parameter estimate is determined by both tracking error and prediction error estimate. Further enhancements are made by incorporating a functional dependence of the a priori value on the intake manifold pressure.