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Current CJ-4 lubricant specifications place chemical limits on diesel engine oil formulations to minimize the accumulation of lubricant-derived ash in diesel particulate filters (DPF). While lubricant additive chemistry plays a strong role in determining the amount and type of ash accumulated in the DPF, a number of additional factors play important roles as well. Relative to soot particles, whose residence time in the DPF is short-lived, ash particles remain in the filter for a significant fraction of the filter's useful life. While it is well-known that the properties (packing density, porosity, permeability) of soot deposits are primarily controlled by the local exhaust conditions at the time of particle deposition in the DPF, the cumulative operating history of the filter plays a much stronger role in controlling the properties and distribution of the accumulated ash.

The increasing use of diesel and gasoline particulate filters requires advanced on-board diagnostics (OBD) to prevent and detect filter failures and malfunctions. Early detection of upstream (engine-out) malfunctions is paramount to preventing irreversible damage to downstream aftertreatment system components. Such early detection can mitigate the failure of the particulate filter resulting in the escape of emissions exceeding permissible limits and extend the component life. However, despite best efforts at early detection and filter failure prevention, the OBD system must also be able to detect filter failures when they occur. In this study, radio frequency (RF) sensors were used to directly monitor the particulate filter state of health for both gasoline particulate filter (GPF) and diesel particulate filter (DPF) applications.

This paper presents a model-based control system for SCR urea dosing employing an embedded real-time SCR chemistry model and a NH3 sensor. The control algorithm consists of a number of control features designed to enhance ammonia storage control and closed-loop compensation using the mid-brick NH3 sensor. An adaptive control algorithm is developed to demonstrate robustness of the feedback control system to compensate for catalyst aging, urea injection malfunction, or dosing fluid concentration variation. Simulation and engine dynamometer testing following ESC and FTP emission cycles are used to demonstrate the advantages of this control approach for meeting both NOx emission requirements and NH3 slip targets. Furthermore this paper demonstrates potential of a NH3 sensor for on-board diagnostics. Additionally the feasibility of implementing model based algorithms in a 32-bit floating-point environment with an automotive controller is examined.

Accurate knowledge of diesel particulate filter (DPF) particulate matter (PM) loading is critical for robust and efficient operation of the combined engine-exhaust aftertreatment system. Furthermore, upcoming on-board diagnostics regulations require on-board technologies to evaluate the status of the DPF. This work describes the application of radio frequency (RF) - based sensing techniques to accurately measure DPF particulate matter levels. A 1.9L GM turbo diesel engine and a DPF with an RF-sensor were studied. Direct comparisons between the RF measurement and conventional pressure-based methods were made. Further analysis of the particulate matter loading rates was obtained with a mass-based total PM emission measurement instrument (TEOM) and DPF gravimetric measurements.

Reliable means for on-board detection of particulate filter failures or malfunctions are needed to meet diagnostics (OBD) requirements. Detecting these failures, which result in tailpipe particulate matter (PM) emissions exceeding the OBD limit, over all operating conditions is challenging. Current approaches employ differential pressure sensors and downstream PM sensors, in combination with particulate filter and engine-out soot models. These conventional monitors typically operate over narrowly-defined time windows and do not provide a direct measure of the filter’s state of health. In contrast, radio frequency (RF) sensors, which transmit a wireless signal through the filter substrate provide a direct means for interrogating the condition of the filter itself.