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Tier 4 emissions legislation is emerging as a clear pre-cursor for widespread adoption of exhaust aftertreatment in off-highway applications. Large bore engine manufacturers are faced with the significant challenge of packaging a multitude of catalyst technologies in essentially the same design envelope as their pre-Tier 4 manifestations, while contending with the fuel consumption consequences of the increased back pressure, as well as the incremental cost and weight associated with the aftertreatment equipment. This paper discusses the use of robust metallic catalysts upstream of the exhaust gas turbine, as an effective means to reduce catalyst volume and hence the weight and cost of the entire aftertreatment package. The primarily steady-state operation of many large bore engine applications reduces the complication of overcoming pre-turbine catalyst thermal inertia under transient operation.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.