Smart DPF Regenerations - A Case Study of a Connected Powertrain Function 2019-01-0316

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). Standard DPF regenerations are scheduled on an inferred soot load based on indirect indicators of system state, such as exhaust gas flow rate and pressure drop across the DPF and/or empirical models of engine out soot. Soot load estimation approaches such as these are necessary since a reliable method of a direct soot load measurement in a DPF is currently not available. In addition to model uncertainty it is also well known that regeneration control also suffers from uncertainty related to the drive routes, driver behavior, and traffic flow over the driven routes. These uncertainties force a conservative regeneration scheme, that does not fully exploit the soot trapping capacity of the DPF. This makes it difficult to guarantee any measure of uniform optimality over all vehicles. It is evident, however, that by leveraging information that allows a reduction in driver and traffic related uncertainties it may be possible to better schedule DPF regenerations and achieve some degree of performance benefit related to the overall efficiency of the regeneration process over the life of the vehicle. In this paper we present some initial results from such an effort that leverages cloud based real time traffic flow information from a traffic provider in making smart decisions related to the soot management over a DPF.