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Exhaust temperatures are some of the hardest parameters to measure and estimate based on the range of the signal and the environment that an engine exhaust system creates. Accurate exhaust temperature inputs in vehicle and engine control systems are important for performance, fuel economy, emissions and aftertreatment control. A turbine inlet exhaust temperature observer model based on isentropic expansion and heat transfer across a turbocharger turbine was developed and investigated in this paper. There are 4 main components used to model the exhaust temperature; an open loop exhaust manifold gas temperature mass/energy model, an isentropic expansion across the turbine, a turbine heat transfer model and an observer using the downstream turbine outlet temperature. Another method using only a reverse isentropic expansion model and heat transfer parts of the observer model was analyzed and compared to the observer model.

The availability of connectivity and autonomy enabled resources, within the automotive sector, has primarily been considered for driver assist technologies and for extending the levels of vehicle autonomy. It is not a stretch to imagine that the additional information, available from connectivity and autonomy, may also be useful in further improving powertrain functions. Critical powertrain subsystems that must operate with limited or uncertain knowledge of their environment stand to benefit from such new information sources. Unfortunately, the adoption of this new information resource has been slow within the powertrain community and has typically been limited to the obvious problem choices such as battery charge management for electric vehicles and efforts related to fuel economy benefits from adaptive/coordinated cruise control. In this paper we discuss the application of connectivity resources in the management of an aftertreatment sub-system, the Diesel Particulate Filter (DPF).