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The advancement of Machine-learning (ML) methods enables data-driven creation of Reduced Order Models (ROMs) for automotive components and systems. For example, Gaussian Process Regression (GPR) has emerged as a powerful tool in recent years for building a static ROM as an alternative to a conventional parametric model or a multi-dimensional look-up table. GPR provides a mathematical framework for probabilistically representing complex non-linear behavior. Today, GPR is available in various programing tools and commercial CAE packages. However, the application of GPR is system dependent and often requires careful design considerations such as selection of input features and specification of kernel functions. Hence there is a need for GPR design optimization driven by application requirements. For example, a moving window size for training must be tuned to balance performance and computational efficiency for tracking changing system behavior.

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).

The resistive particulate matter sensor (PMS) is rapidly becoming ubiquitous on diesel vehicles as a means to diagnose particulate filter (DPF) leaks. By design the device provides an integrated measure of the amount of PM to which it has been exposed during a defined measurement period within a drive cycle. The state of the art resistive PMS has a large deadband before any valid output related to the accumulated PM is realized. As a result, most DPF monitors that use the PMS consider its output only as an indicator that a threshold quantity of PM has amassed rather than a real-time measure of concentration. This measurement paradigm has the unfortunate side effect that as the PM OBD threshold decreases, or the PMS is used on a vehicle with a larger exhaust volume flow, a longer measurement is required to reach the same PM sensor output. Longer PMS measurement times lead to long particulate filter monitoring durations that may reduce filter monitor completion frequency.