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 Therefore, MBC is about to be superseded by manual parameterization methods.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. By removing the time factor from the controller's performance, it enables the MBC to model the system performance and optimize the controller's parameters. The benefits of modeling a system's performance using MBC approach, are employing radial basis function network which is known for its modeling accuracy for highly non-linear systems. This paper presents the working principle of calibrating a PI controller for a fuel transport delay plant using Model-based calibration method. The PI controller is used for controlling the injected fuel mass flow depending on the lambda set point input. The outcome of the automatic calibration process is a series of optimized gain scheduled tables for the fuel PI controller. This paper is to prove there's fine line between calibrating a statistical and dynamical system using Model-based Calibration method.
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