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The recent development of diesel engine fuel injection systems has been dominated by how to manage the degrees of freedom that common rail multi-pulse systems now offer. A number of production engines already use four injection events while in research, work based on up to eight injection events has been reported. It is the degrees of freedom that lead to a novel experimental requirements. There is a potentially complex experimental program needed to simply understand how injection parameters influence the combustion process in steady state. Combustion behavior is not a continuum and as both injection and EGR rates are adjusted, distinct combustion modes emerge. Conventional calibration processes are severely challenged in the face of large number of degrees of freedom and as a consequence new development approaches are needed.

The notion of open source systems has been well established in systems software and typified by the development of the Linux operating system. An open source community is a community of interest that makes use of software tools in research and development. Their ongoing development is part of the free flow of ideas on which the community. The motivation for the work reported in this paper is to provide the research community in engine controls with a ready access to a complete engine management solution and the component parts. The work described in this paper extends open source principles to engine control with a portable spark ignition (SI) control strategy assembled using Simulink. The underlying low level drivers are written in C and designed for portability. A calibration tool is written in C and works over a controller area network (CAN) link to the engine control unit (ECU). The ECU hardware is based on the Infineon Tricore microcontroller.

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.