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Uncertainty Analysis of Resistive Soot Sensors for On-Board Diagnostics of Automotive Particulate Filters

Baradwaj, Nithin V
http://rave.ohiolink.edu/etdc/view?acc_num=osu1376408721

Abstract Details

2013, Master of Science, Ohio State University, Mechanical Engineering.
The on-board diagnostic (OBD) regulations associated with diagnosing diesel particulate filter (DPF) failures have become more stringent in the United States. Additionally, similar filter technology may be developed for use in gasoline engines. Pressure drop across the DPF has been typically used to diagnose DPF failures. Tighter OBD regulations mandate a new direct measurement of soot downstream of DPF in order to measure the filtering efficiency to detect failures as soon as it occurs. New particulate matter (PM) sensors have been developed by several companies in order to detect the particles passing through the DPF. Of these, resistive type soot sensors have shown great potential to sense and measure small quantities of soot with good precision and accuracy while meeting the cost constraints for the automobile industry.A virtual model of the soot sensor has been developed to simulate the behavior of the actual sensor in relevant diesel exhaust conditions as well as output a response time which is dependent on the mass flowrate of soot in the exhaust gas stream. The virtual sensor model is calibrated against experimental data provided by a sensor manufacturer in order to reduce the systematic errors due to assumptions and approximations considered during model development. A sensor transfer function, which is a black box model, is developed to estimate the mass of soot in the exhaust gas stream as a function of parameters from the ECU and the soot sensor. The transfer function is a second degree multivariable polynomial surface fit which predicts the mass of soot from parameters available on-board in a vehicle. Sensitivity studies are conducted in order to understand how a change in input parameters affect the outputs of the model and the transfer function. Since the outputs were anticipated to vary non-linearly and asymmetrically with respect to the inputs, several perturbation levels of the input parameters were used for the sensitivity study for each variable. Uncertainties in sensor response time and estimated mass of soot have been identified and quantified. The uncertainties in sensor response time have been determined using sequential perturbation method whereas, the uncertainties in the estimated mass of soot have been determined using analytical methods. The important factors which contribute towards the uncertainty in estimated mass of soot have been identified and their effect on the total uncertainty in estimated mass of soot is discussed. Quantification of uncertainties in estimated mass of soot is of prime importance for a good OBD strategy to detect DPF failures considering stringent PM OBD regulations. The uncertainty in response times determined using the virtual sensor model was found to be around 6\% whereas, the uncertainty in response times determined from experiments data was found to be around 12\%. The uncertainty in the estimated mass of soot using the transfer function was found to range from 25 \% - 31 \%. Uncertainties involved in the measurement of input parameters for the calibration of the transfer function was found to be the major contributor to the total uncertainty in the estimated mass of soot.
Shawn Midlam-Mohler, Dr. (Advisor)
Giorgio Rizzoni, Prof. (Committee Member)
Fabio Chiara, Dr. (Committee Member)
137 p.
Automotive Engineering; Engineering
Soot, Sensor, Uncertainty, On-Board Diagnostics, PM, Emissions, DPF, OBD

Recommended Citations

Citations

  • Baradwaj, N. V. (2013). Uncertainty Analysis of Resistive Soot Sensors for On-Board Diagnostics of Automotive Particulate Filters [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376408721

    APA Style (7th edition)

  • Baradwaj, Nithin. Uncertainty Analysis of Resistive Soot Sensors for On-Board Diagnostics of Automotive Particulate Filters. 2013. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1376408721.

    MLA Style (8th edition)

  • Baradwaj, Nithin. "Uncertainty Analysis of Resistive Soot Sensors for On-Board Diagnostics of Automotive Particulate Filters." Master's thesis, Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376408721

    Chicago Manual of Style (17th edition)

osu1376408721
469
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This open access ETD is published by The Ohio State University and OhioLINK.