Dejan Kihas

Toward a Systematic Design for Turbocharged Engine Control

The efficient development of high performance control is becoming more important and more challenging with ever tightening emissions legislation and increasingly complex engines. Many traditional industrial control design techniques have difficulty in addressing multivariable interactions among subsystems and are becoming a bottleneck in terms of development time. In this article we explore the requirements imposed on control design from a variety of sources: the physics of the engine, the embedded software limitations, the existing software hierarchy, and standard industrial control development processes. Decisions regarding the introduction of any new control paradigm must consider balancing this diverse set of requirements. In this context we then provide an overview of our work in developing a systematic approach to the design of optimal multivariable control for air handling in turbocharged engines.

Selection and tuning of a reduced parameter set for a turbocharged diesel engine model

This paper presents an approach for systematic reduced-parameter-set adaption applicable to internal combustion engine models. The presented idea is to detect the most influential parameters in an engine air-charge path model and then use them as a reduced-parameter-set for further calibration to improve the model accuracy. Since only most influential parameters (in comparison with a complete set of parameters) are tuned at the final calibration process, this approach helps reducing over-parameterization associated with tuning highly nonlinear engine models. Detection of the influential parameters is done using the sensitivity analysis followed by the principle component analysis. Accuracy of the reduced-parameter-set tuned model is compared to a model with a tuned full-parameter-set developed following commercially available OnRAMP Design Suite [1] methodology. Results from experiments on a heavy duty diesel (HDD) engine show that although tuning the full-parameter-set (with over 70 parameters) creates higher accuracy, an average of 50% improvement of the model accuracy is attained using the proposed reduced-parameter-set approach (which tunes only 2 parameters).