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Model-based calibration (MBC) is a systematic method to calibrate an engine control unit (ECU) system. Due to the working principle of MBC, it is only being used for steady-state systems (time independent models). This limits the use of MBC; because an ECU contains statistical and dynamical systems. Due to the limitations of MBC, dynamical systems require manual tuning which may be time-consuming. With the increasing popularity in hybrid and electrical vehicle systems, most of them rely on dynamical systems. Therefore, MBC is about to be superseded by manual parameterization methods. Remarkably, MBC is not limited to the steady state systems. It can be achieved by separating the time factor of a system and extracting the statistical data from a time series measurement. Typically, MBC model is conceived as the representation of a system plant (i.e.: air path, fuel path, mean value engine model). As a matter of fact, MBC model is not limited to identification of system plant.

A full calibration exercise of a diesel engine air path can take months to complete (depending on the number of variables). Model-based calibration approach can speed up the calibration process significantly. This paper discusses the overall calibration process of the air-path of the Cat® C7.1 engine using statistical machine learning tool. The standard Cat® C7.1 engine's twin-stage turbocharger was replaced by a VTG (Variable Turbine Geometry) as part of an evaluation of a novel air system. The changes made to the air-path system required a recalculation of the air path's boost set point and desired EGR set point maps. Statistical learning processes provided a firm basis to model and optimize the air path set point maps and allowed a healthy balance to be struck between the resources required for the exercise and the resulting data quality.