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This study reassesses an overview of the potential of the radio frequency (RF)-based state diagnostics of three-way catalysts (TWC) based on a previous study with an emphasis on the defect chemistry of the catalyst material during reoxidation and reduction. Some data are based on the previous works but are newly processed, and the signal parameters resonant frequency and inverse quality factor are evaluated with respect to applicability. The RF-based method uses electromagnetic resonances in a cavity resonator to provide information on the storage level of the oxygen storage component. The analysis focuses on a holistic investigation and evaluation of the major effects influencing the RF signal during operation. On the one hand, the response to the oxygen storage behavior and the resolution of the measurement method are considered. Therefore, this study merges original data from multiple former publications to provide a comprehensive insight into important measurement effects and their defect chemistry background. On the other hand, the most important cross-sensitivities are discussed and their impact during operation is evaluated. Additionally, the effect of catalyst aging is analyzed. The effects are presented separately for the two resonant parameters: resonant frequency and (unloaded) quality factor. Overall, the data suggest that the quality factor has a way higher signal quality at low temperatures (<400 °C) and the resonant frequency is primarily suitable for high operating temperatures. At most operating points, the quality factor is even more robust against interferences such as exhaust gas stoichiometry and water content. Correctly estimating the catalyst temperature is the most important factor for reliable results, which can be achieved by combining the information of both resonant signals. In the end, the data indicate that microwave-based state diagnosis is a powerful system for evaluating the oxygen storage level over the entire operating range of a TWC. As a research tool and in its application, the system can therefore contribute to the improvement of the emission control of future gasoline vehicles.

Recently, radio frequency (RF) technology was introduced as a tool to determine the oxygen storage level of a three-way catalyst (TWC) for gasoline vehicles. Previous studies on the investigation of commercial catalysts mostly use only the resonant frequency to describe the correlation of oxygen storage level and RF signal. For the first time this study presents a comparison under defined laboratory conditions considering both, resonance frequency and also the quality factor as measurands. Furthermore, various advantages over the sole use of the resonant frequency in the technical application are discussed. Experiments with Ø4.66’’ catalysts and Ø1.66’’ catalyst cores with alternating (rich/lean) gas compositions showed that the relative change in signal amplitude due to a change in oxygen storage is about 100 times higher for the inverse quality factor compared to the resonant frequency. In addition, the quality factor reacts more sensitively to the onset of the oxygen-storage ability, and delivers precise information about the necessary temperature, which is not possible when evaluating the resonant frequency due to the low signal amplitude. As investigations on aged catalysts confirm, the quality factor also provides a new approach to determine operando the ageing state of a TWC.

The accumulating-type (or integrating-type) NOx sensor principle offers two operation modes to measure low levels of NOx: The direct signal gives the total amount dosed over a time interval and its derivative the instantaneous concentration. With a linear sensor response, no baseline drift, and both response times and recovery times in the range of the gas exchange time of the test bench (5 to 7 s), the integrating sensor is well suited to reliably detect low levels of NOx. Experimental results are presented demonstrating the sensor’s integrating properties for the total amount detection and its sensitivity to both NO and to NO2. We also show the correlation between the derivative of the sensor signal and the known gas concentration. The long-term detection of NOx in the sub-ppm range (e.g., for air quality measurements) is discussed. Additionally, a self-adaption of the measurement range taking advantage of the temperature dependency of the sensitivity is addressed.

Current developments in exhaust gas aftertreatment led to a huge mistrust in diesel driven passenger cars due to their NOx emissions being too high. The selective catalytic reduction (SCR) with ammonia (NH3) as reducing agent is the only approach today with the capability to meet upcoming emission limits. Therefore, the radio-frequency-based (RF) catalyst state determination to monitor the NH3 loading on SCR catalysts has a huge potential in emission reduction. Recent work on this topic proved the basic capability of this technique under realistic conditions on an engine test bench. In these studies, an RF system calibration for the serial type SCR catalyst Cu-SSZ-13 was developed and different approaches for a temperature dependent NH3 storage were determined. This paper continues this work and uses a fully calibrated RF-SCR system under transient conditions to compare different directly measured and controlled NH3 storage levels, and NH3 target curves. It could be clearly demonstrated that the right NH3 target curve, together with a direct control on the desired level by the RF system, is able to operate the SCR system with the maximum possible NOx conversion efficiency and without NH3 slip.

The upcoming more stringent automotive emission legislations and current developments have promoted new technologies for more precise and reliable catalyst control. For this purpose, radio-frequency-based (RF) catalyst state determination offers the only approach for directly measuring the NH3 loading on selective catalytic reduction (SCR) catalysts and the state of other catalysts and filter systems. Recently, the ability of this technique to directly control the urea dosing on a current NH3 storing zeolite catalyst has been demonstrated on an engine dynamometer for the first time and this paper continues that work. Therefore, a well-known serial-type and zeolite-based SCR catalyst (Cu-SSZ-13) was investigated under deliberately chosen high space velocities. At first, the full functionality of the RF system with Cu-SSZ-13 as sample was tested successfully. By direct RF-based NH3 storage control, the influence of the storage degree on the catalyst performance, i.e., on NOx conversion and NH3 slip, was investigated in a temperature range between 250 and 400 °C. For each operation point, an ideal and a critical NH3 storage degree was found and analyzed in the whole temperature range. Based on the data of all experimental runs, temperature dependent calibration functions were developed as a basis for upcoming tests under transient conditions. Additionally, the influence of exhaust humidity was observed with special focus on cold start water and its effects to the RF signals.

An impedimetric NOx dosimeter based on the NOx sorption material KMnO4 is proposed. In addition to its application as a low level NOx dosimeter, KMnO4 shows potential as a precious metal free lean NOx trap material (LNT) for NOx storage catalysts (NSC) enabling electrical in-situ diagnostics. With this dosimeter, low levels of NO and NO2 exposure can be detected electrically as instantaneous values at 380 °C by progressive NOx accumulation in the KMnO4 based sensitive layer. The linear NOx sensing characteristics are recovered periodically by heating to 650 °C or switching to rich atmospheres. Further insight into the NOx sorption-dependent conductivity of the KMnO4-based material is obtained by the novel eTPD method that combines electrical characterization with classical temperature programmed desorption (TPD). The NOx loading amount increases proportionally to the NOx exposure time at sorption temperature. The cumulated NOx exposure, as well as the corresponding NOx loading state, can be detected linearly by electrical means in two modes: (1) time-continuously during the sorption interval including NOx concentration information from the signal derivative or (2) during the short-term thermal NOx release.

A novel and promising method to measure low levels of NOx utilizes the accumulating sensor principle. During an integration cycle, incoming NOx molecules are stored in a sensitive layer based on an automotive lean NOx trap (LNT) material that changes its electrical resistivity proportional to the amount of stored NOx, making the sensor suitable for long-term detection of low levels of NOx. In this study, the influence of the thickness of the sensitive layer, prepared by multiple screen-printing, is investigated. All samples show good accumulating sensing properties for both NO and NO2. In accordance to a simplified model, the base resistance of the sensitive layer and the sensitivity to NOx decrease with increasing thickness. Contrarily, the sensor response time increases. The linear measurement range of all samples ends at a sensor response of about 30% resulting in an increase of the linearly detectable amount with the thickness. Hence, the variation of the thickness of the sensitive layer is a powerful tool to adapt the linear measurement range (proportional to the thickness) as well as the sensitivity (proportional to the inverse thickness) to the application requirements. Calculations combining the sensor model with the measurement results indicate that for operation in the linear range, about 3% of the LNT material is converted to nitrate.

In this work, tailpipe carbon monoxide emission from a gasoline powertrain case study vehicle was analyzed for off-cycle (i.e., on road) driving to develop a virtual sensor. The vehicle was equipped with a portable emissions measurement system (PEMS) that measured carbon monoxide concentration and exhaust volumetric flowrate to calculate the mass of carbon monoxide emitted from the tailpipe. The vehicle was also equipped with a tailpipe electrochemical NOx sensor, and a correlation between its linear oxygen signal and the PEMS-measured carbon monoxide concentration was observed. The NOx sensor linear oxygen signal depends on the concentration of several reducing species, and a machine learning model was trained using this data and other features to target the PEMS-measured carbon monoxide mass emission. The model demonstrated a mean absolute percentage error (MAPE) of 19% when using 15 training drive cycles. Finally, a virtual carbon monoxide sensor was developed by removing the tailpipe NOx sensor information from the model feature set and predicting tailpipe carbon monoxide mass. The virtual model MAPE was shown to increase by 5% compared to the earlier version with a tailpipe NOx sensor over the same number of training, validation, and test drive cycles. The minimal degradation in accuracy for the virtual model was hypothesized to result from the fact that narrowband oxygen sensors may contain information regarding how rich or lean the exhaust gas is compared to stoichiometric conditions. This is analogous to the information provided by a wide-band oxygen sensor, but potentially with reduced resolution and accuracy. The data-driven approach was able to produce a novel virtual tailpipe carbon monoxide sensor in a gasoline powertrain using only common powertrain and emission sensors.

Future automotive emission regulations are becoming increasingly dependent on off-cycle (acquired on road and referred to as “real-world”) driving and testing. This was driven in part by the often-observed fact that laboratory emission drive cycles (developed to evaluate a vehicle’s emissions on a chassis dynamometer) may not fully capture the nature of real-world driving. As a result, portable emission measurement systems were developed that could be fit in the trunk of a vehicle, but were relatively large, expensive, and complex to operate. It would be advantageous to have low-cost and simple to operate on-board sensors that could be used in a gasoline powertrain to monitor important criteria emission species, such as NOx. The electrochemical NOx sensor is often used for emissions control systems in diesel powertrains and a proven technology for application to the relatively harsh environment of automotive exhaust. However, electrochemical NOx sensors are nearly equally sensitive to both NOx and NH3, setting up an implicit classification problem that must be solved before they can accurately measure NOx. In this work, we develop a machine-learning model to classify the output of a NOx sensor in a gasoline powertrain. A model generalization study is conducted, and the model is found to be ~96% accurate and able to predict NOx mass emitted over a drive cycle within ~9% of a perfectly classified NOx sensor.

Ammonia adsorption on the catalyst surface is a crucial step in the selective catalytic reduction of nitrogen oxides over zeolites with NH₃ as the reducing agent. In this study, two small pore zeolites with chabazite frameworks, H-SSZ-13 and Cu exchanged SSZ-13, are examined. Adsorption of NH₃ on the zeolite causes changing electrical properties of the material. They can be detected by a radio frequency based technique. We have found that with this method it is possible to determine the amount of adsorbed NH₃ on these catalysts, examining both the influences of temperature and NH₃/NO feed gas ratio. At constant temperature, a fairly linear correlation between the resonance frequency and the amount of adsorbed ammonia was observed. Furthermore, this method also allows differentiation between some of the NH₃ adsorption sites.