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A Deep Learning-Based Strategy to Initiate Diesel Particle Filter Regeneration
Abstract:
Deep learning (DL)-based approaches enable unprecedented control paradigms for
propulsion systems, utilizing recent advances in high-performance computing
infrastructure connected to modern vehicles. These approaches can be employed to
optimize diesel aftertreatment control systems targeting the reduction of
emissions. The optimization of the Trapped Soot Load (TSL) reduction in the
Diesel Particulate Filter (DPF) is such an example. As part of the diesel
aftertreatment system, the DPF stores the soot particles resulting from the
combustion process in the engine. Periodically, the stored soot is oxidized
during a DPF regeneration event. The efficiency of such a regeneration
influences the fuel economy, and potentially the service interval of the
vehicle. The quality of a regeneration depends on the operating conditions of
the DPF, the engine, and the ability to complete the regeneration event. The
favorable occurrence of these conditions is determined by a high number of
variables including the speed profile, the state of the road, and the influence
of traffic conditions. Control algorithms aim to find the drive cycle intervals
with optimal conditions for executing a regeneration. It is a challenging task
to optimize regenerations using rule-based control approaches. Such algorithms
have a limited capability to handle a wide variety of drive cycles. This article
proposes a DL-based control strategy that aims to reduce oil dilution while
increasing fuel efficiency by minimizing the number of regenerations and
maximizing the oxidized soot load. Based on the analysis of the driving
conditions, the proposed strategy targets the most conducive regeneration
opportunities. The proposed strategy is evaluated using historical drive cycle
data of 10 vehicles, covering a year of vehicle operation. The effectiveness of
the DL-based control approach compared to a rule-based control strategy is
discussed. The results show that the DL-based control approach leads to fewer
interrupted regenerations and more soot oxidation per regeneration while
reducing oil dilution and increasing fuel efficiency.