The objective of this paper is to design static optimal control maps of diesel engines to achieve high efficiency and emission reduction. The calibration tool applied to create the control maps, named "Off-line parameterization tool", was designed based on the Design of Experiments method. The optimization goal is to minimize the Brake Specific Fuel Consumption (BSFC) by the engine's input parameters under emission constraints. The tool was designed to work both fully automatically and semi-automatically. Many reports on engine calibration have taken the Design of Experiments (DoE) approach, but their implementations in choosing experimental design types and optimization processes are different compared to this paper. The unique aspect of this research lies in the significant properties of the Off-line parameterization tool. First, this tool is flexible: it is able to work with multiple inputs and multiple outputs. Second, it can reduce the calibration time as the engine running time is kept as short as possible and all the data processing work is accomplished automatically.

This paper presents an approach to a new engine calibration method that takes the engine’s operational profile into account. This method has two main steps: modeling and optimization. The Design of Experiments method is first conducted to model the engine’s responses such as Brake Specific Fuel Consumption (BSFC) and Nitrogen Oxide ( N O x ) emissions as the functions of fuel injection timing, common rail pressure and charged air pressure. These response surface models are then used to minimize the fuel consumption during a year, according to a typical load profile of a ferry, and to fulfill the N O x limits set by International Maritime Organization (IMO) regulations, Tier II, test cycle E2. The Sequential Quadratic Programming algorithm is used to solve this minimization problem. The results showed that the fuel consumption can be effectively reduced with the flexibility to trade it off with the N O x emissions while still fulfilling the IMO regulations. In general, this method can decrease the manual calibration effort and improve the engine’s performance with a tailored setting for individual operational profiles.

In this work, a model predictive controller is developed for a multiple injection combustion model. A 1D engine model with three distinct injections is used to generate data for identifying the state-space representation of the engine model. This state-space model is then used to design a controller for controlling the start of injection and injected fuel mass of the post injection. These parameters are used as inputs for the engine model to control the maximum cylinder pressure and indicated mean effective pressure.