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Technical Paper

Particulate Filter Soot Load Measurements using Radio Frequency Sensors and Potential for Improved Filter Management

2016-04-05
2016-01-0943
Efficient aftertreatment management requires accurate sensing of both particulate filter soot and ash levels for optimized feedback control. Currently a combination of pressure drop measurements and predictive models are used to indirectly estimate the loading state of the filter. Accurate determination of filter soot loading levels is challenging under certain operating conditions, particularly following partial regeneration events and at low flow rate (idle) conditions. This work applied radio frequency (RF)-based sensors to provide a direct measure of the particulate filter soot levels in situ. Direct measurements of the filter loading state enable advanced feedback controls to optimize the combined engine and aftertreatment system for improved DPF management. This study instrumented several cordierite and aluminum titanate diesel particulate filters with RF sensors. The systems were tested on a range of light- and heavy-duty applications, which included on- and off-road engines.
Technical Paper

Continuous Particulate Filter State of Health Monitoring Using Radio Frequency Sensing

2018-04-03
2018-01-1260
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.
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