Real-time Sensing of Particulate Matter in a Vehicle Exhaust System

Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
Onboard diagnostic regulations require performance monitoring of diesel particulate filters used in vehicle aftertreatment systems. Delphi has developed a particulate matter (PM) sensor to perform this function. The objective of this sensor is to monitor the soot (PM) concentration in the exhaust downstream of the diesel particulate filter which provides a means to calculate filter efficiency. The particulate matter sensor monitors the deposition of soot on its internal sensing element by measuring the resistance of the deposit. Correlations are established between the soot resistance and soot mass deposited on the sensing element. Currently, the sensor provides the time interval between sensor regeneration cycles, which, with the knowledge of the exhaust gas flow parameters, is correlated to the average soot concentration. Recent advancements in the filtering and processing of the PM Sensor output signal now allow for translation of the sensor resistance signal into real time instantaneous soot mass flow, soot flux, and soot concentration which are available throughout the active zone of the sensor cycle. Additional algorithms provide cumulative soot mass, average flux and average concentration during the sensor cycle and also for the duration of the vehicle drive cycle. This paper describes the recent advances in the PM Sensor signal processing algorithms along with their implementation on a real-time PM Sensor Development Controller. A summary of algorithm performance in comparison to laboratory instrumentation is provided. Finally, an evaluation of the potential benefits of the new technology is presented along with planned next steps.
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DOI
https://doi.org/10.4271/2017-01-1639
Pages
14
Citation
Malaczynski, G., and Roth, G., "Real-time Sensing of Particulate Matter in a Vehicle Exhaust System," SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 10(1):216-230, 2017, https://doi.org/10.4271/2017-01-1639.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-1639
Content Type
Journal Article
Language
English