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).
['Rasoul Salehi', 'Anna G. Stefanopoulou', 'Dejan Kihas', 'Michael Uchanski']
Selection and tuning of a reduced parameter set for a turbocharged diesel engine model