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Abstract-A nonlinear system identification procedure, based on a polynomial NARMAX representation, is applied to a variable geometry turbocharged diesel engine. The relation between the variable geometry turbine (VGT) command and the intake manifold air pressure is described by a nonlinear model, directly identified from raw data. The intent of the paper is to explore the advantages of such a modeling procedure in automotive applications in terms of efficiency and complexity, in view of the related controller design and tuning problem. Simulation results on a HDI diesel engine model illustrate the whole procedure.

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In this paper a nonlinear system identification methodology based on a polynomial NARMAX model representation is considered. Algorithms for structure selection and parameter estimation are presented and evaluated. The goal of the procedure is to provide a nonlinear model characterized by a low complexity and that can be efficiently used in industrial applications. The methodology is illustrated by means of an automotive case study, namely a variable geometry turbocharged diesel engine. The nonlinear model representing the relation between the variable geometry turbine command and the intake manifold air pressure is identified from data and validated.

Control algorithms for hybrid vehicles have undergone extensive research and development leading to near-optimal techniques being employed and demonstrated in prototype vehicles over the previous decade. The use of different implementations of optimal controllers is inevitably linked through the assumed knowledge of the system being controlled. With the growing interest in alterna-tive fuels, such as ethanol, liquified petroleum gas (LPG), and com-pressed natural gas (CNG) due to enhanced emissions and fuel security considerations, a natural extension is to hybridize these engines to improve fuel economy and CO2 emissions. This step is complicated by the potential variation in fuel composition seen with many gasoline and diesel alternatives, leading to uncertainty in the models used by the hybrid powertrain controller. This work investigates the robustness of one hybrid powertrain optimal con-trol approach, the equivalent consumption minimization strategy (ECMS). Two case studies are performed involving experimentally obtained engine maps from two significantly different prototypes flex-fuel vehicles to quantify the potential impact of map error caused by incorrect fuel assumptions. [DOI: 10.1115/1.4027561]