A Bayesian Estimation Model for Transient Engine Exhaust Characterization Using Fourier Transform Infrared Spectroscopy

Abstract

Comprehensive emissions models extensively use engine exhaust data from vehicle experiments to represent the relationship between fuel composition and pollutants. However, the predicted emissions from these models often neglect the effects of transients and speed-load history. Fourier transform infrared (FTIR) spectroscopy is a high frequency measurement technique capable of comprehensive speciation. However, due to long residence times of exhaust within a FTIR spectrometer gas cell, FTIR measurements are contaminated by the effects of historical emissions, precluding the attainment of time-resolved engine exhaust data. This work presents a Bayesian estimation model for processing FTIR measurements to obtain accurate estimations of instantaneous engine exhaust composition. The Bayesian model utilizes a simple model of the mixing dynamics of the gas cell and measurement noise statistics to estimate the composition of exhaust entering the FTIR gas cell during a measurement period. To validate the estimation model, synthetic FTIR measurements are generated from simulated engine exhaust data from the Federal Test Procedure driving cycle. These synthetic measurements are processed by the estimation model, which is shown to yield improved estimations of instantaneous composition as compared to the raw FTIR measurements, although the degree of improvement depends on the magnitude of measurement noise and flow rate through the FTIR gas cell. For a measurement noise standard deviation of 0.5% of the maximum measurement, the estimation model improved estimates of instantaneous NO emission by approximately 42.5% on average, while about a 7.5% improvement was achieved for a measurement noise standard deviation of 2% of the maximum measurement for a FTIR flow rate of 10 L/min. For a flow rate of 25 L/min, improvements of approximately 41.5% and 6% were achieved for measurement noise standard deviations of 0.5% and 2% of the maximum measurements, respectively. The application of the model in this work is to generate time-resolved emissions estimates to further elucidate the relationship between fuel composition and engine emissions.

Exploration of the Hysteresis in Speciated Emissions during Transient Gasoline Engine Combustion

“…The limitations of FTIR spectroscopy regarding transient analysis are addressed with the implementation of a previously developed UKF, which has been experimentally validated against numerous measurements of compositionally evolving samples. This algorithm and its validation have been thoroughly detailed in previous works − and are therefore only briefly explained here. A UKF is an algorithm that couples model predictions with measurements to obtain statistically optimized estimations of the states of a system.…”

“…The model predicts VOC emissions according to the map value that corresponds to the current engine speed and load. To address sample recirculation and spectral IR intensity stationarity issues associated with FTIR measurements of the sample with rapidly evolving composition, a previously developed unscented Kalman filter (UKF) , is employed to filter FTIR measurements. This algorithm utilizes simple models for residence time distribution and sample absorbance evolution to estimate the composition of the sample entering the FTIR gas cell during a measurement period, thereby enabling time-resolved estimates of comprehensive emissions.…”

Experimental Validation of an Unscented Kalman Filter for Estimating Transient Engine Exhaust Composition with Fourier Transform Infrared Spectroscopy

“…A high-level overview of the UKF is presented, followed by a detailed discussion of the state transition and measurement models for the FTIR gas cell system. Details of the UKF and its fundamental equations are omitted for brevity, but interested readers are referred to the following literature. − …”

“…The residence time issue can be overcome by estimating the composition of sample entering the FTIR gas cell ( Z in in Figure ) during a measurement period according to the evolution of the measured total gas cell composition ( Z cell ). , This method requires a residence time distribution model, which provides a relationship between inlet and total gas cell composition. Previous work has shown that the well-mixed model sufficiently represents this relationship for typical flow rates . While this method improves the overall trend in estimated instantaneous engine exhaust compared to raw measurements, noise from the FTIR measurements greatly exacerbates in inlet composition estimations, resulting in considerable errors .…”

“…In previous work, an unscented Kalman filter (UKF) for estimating transient composition profiles from FTIR measurements was developed . An UKF is a specific kind of BEM for estimating the states of nonlinear systems, such as an FTIR gas cell represented by a well-mixed model.…”