Particulate Filter Soot Load Measurements using Radio Frequency Sensors and Potential for Improved Filter Management 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. RF sensor accuracy was evaluated relative to a gravimetric standard over a range of soot loading and regeneration conditions, which included partial regenerations and drop-to-idle events. The results were compared to pressure- and model-based estimates of the filter loading state, and indicate considerable potential to enable an adaptive feedback control system capable of directly responding to the filter loading state even under relatively severe operating conditions. Direct feedback control based on in situ measurements of filter soot levels over a broad range of operating conditions may allow for the development of filter management strategies to improve filter durability and reduce engine fuel consumption relative to current pressure-drop and model based approaches.
Citation: Ragaller, P., Sappok, A., Bromberg, L., Gunasekaran, N. et al., "Particulate Filter Soot Load Measurements using Radio Frequency Sensors and Potential for Improved Filter Management," SAE Technical Paper 2016-01-0943, 2016, https://doi.org/10.4271/2016-01-0943. Download Citation
Paul Ragaller, Alexander Sappok, Leslie Bromberg, Natarajan Gunasekaran, Jason Warkins, Ryan Wilhelm
CTS Corporation Boston Innovation Office, Massachusetts Institute of Technology, Corning Inc.