Smart DPF regenerations – A case study of Connected Powertrain functions 2019-01-0316
The availability of connectivity and autonomy enabled resources to the automotive sector, has primarily been considered for driver assist technologies (DAT) and for extending the levels of vehicle autonomy. It is clear, however, that the additional information available from connectivity and autonomy, may also be useful in further improving powertrain functions. Additionally, critical subsystems that must operate with limited or uncertain knowledge of their environment stand to benefit from such new information sources. In this paper we discuss one such system, the Diesel Particulate Filter (DPF). Standard DPF regenerations are scheduled on some soot load inference based on indirect indicators of system state, such as exhaust gas flow rate and pressure drop across the DPF. Approaches such as this are necessary since a reliable method of a direct soot load measurement in the DPF is currently not available. It is also well known that these approaches suffer from uncertainty related to the drive routes, driver behavior and traffic flow over the driven routes. These uncertainties force a conservative regeneration scheme thereby making it very difficult to achieve any measure of optimality. It is evident that by leveraging information that allows 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 real time traffic flow information from a traffic provider in making smart decisions related to the soot management over a DPF.
Michael Hopka, Devesh Upadhyay, Michiel Van Nieuwstadt