Search Results

Radio frequency (RF)-based sensors provide a direct measure of the particulate filter loading state. In contrast to particulate matter (PM) sensors, which monitor the concentration of PM in the exhaust gas stream for on-board diagnostics purposes, RF sensors have historically been applied to monitor and control the particulate filter regeneration process. This work developed an RF-based particulate filter control system utilizing both conventional and fast response RF sensors, and evaluated the feasibility of applying fast-response RF sensors to provide a real-time measurement of engine-out PM emissions. Testing with a light-duty diesel engine equipped with fast response RF sensors investigated the potential to utilize the particulate filter itself as an engine-out soot sensor.

Diesel Particulate Filters (DPF) are a key component in many on- and off-road aftertreatment systems to meet increasingly stringent particle emissions limits. Efficient thermal management and regeneration control is critical for reliable and cost-effective operation of the combined engine and aftertreatment system. Conventional DPF control systems predominantly rely on a combination of filter pressure drop measurements and predictive models to indirectly estimate the soot loading state of the filter. Over time, the build-up of incombustible ash, primarily derived from metal-containing lubricant additives, accumulates in the filter to levels far exceeding the DPF's soot storage limit. The combined effects of soot and ash build-up dynamically impact the filter's pressure drop response, service life, and fuel consumption, and must be accurately accounted for in order to optimize engine and aftertreatment system performance.

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.

The increasing use of diesel and gasoline particulate filters requires advanced on-board diagnostics (OBD) to prevent and detect filter failures and malfunctions. Early detection of upstream (engine-out) malfunctions is paramount to preventing irreversible damage to downstream aftertreatment system components. Such early detection can mitigate the failure of the particulate filter resulting in the escape of emissions exceeding permissible limits and extend the component life. However, despite best efforts at early detection and filter failure prevention, the OBD system must also be able to detect filter failures when they occur. In this study, radio frequency (RF) sensors were used to directly monitor the particulate filter state of health for both gasoline particulate filter (GPF) and diesel particulate filter (DPF) applications.

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels.

This research investigates the combustion characteristics and engine performance of a conventional non-ethanol gasoline with a research octane number of 91(RON 91) and a higher reactivity RON80 gasoline under mixing-controlled combustion. The work was conducted in a model year 2013 Cummins ISX15 heavy-duty diesel engine. A split fuel injection strategy was developed to address the long ignition delay and high maximum pressure rise rate for the two gasoline fuels. Using the split fuel injection strategy, steady-state NOx sweeps were conducted at 1375 rpm with a load sweep from 5 to 15 bar BMEP. At 5 and 10 bar BMEP, both gasolines consistently exhibited lower soot levels than ULSD with the reduction more pronounced at 5 bar BMEP. 3-D CFD combustion simulation suggested that the higher volatility and lower viscosity of gasoline fuels can help improve the in-cylinder air utilization and therefore reduce the presence of fuel-rich regions in the combustion chamber.

The combustion system of a heavy-duty diesel engine operated in a gasoline compression ignition mode was optimized using a CFD-based response surface methodology and a machine learning genetic algorithm. One common dataset obtained from a CFD design of experiment campaign was used to construct response surfaces and train machine learning models. 128 designs were included in the campaign and were evaluated across three engine load conditions using the CONVERGE CFD solver. The design variables included piston bowl geometry, injector specifications, and swirl ratio, and the objective variables were fuel consumption, criteria emissions, and mechanical design constraints. In this study, the two approaches were extensively investigated and applied to a common dataset. The response surface-based approach utilized a combination of three modeling techniques to construct response surfaces to enhance the performance of predictions.

Accurate knowledge of diesel particulate filter (DPF) particulate matter (PM) loading is critical for robust and efficient operation of the combined engine-exhaust aftertreatment system. Furthermore, upcoming on-board diagnostics regulations require on-board technologies to evaluate the status of the DPF. This work describes the application of radio frequency (RF) - based sensing techniques to accurately measure DPF particulate matter levels. A 1.9L GM turbo diesel engine and a DPF with an RF-sensor were studied. Direct comparisons between the RF measurement and conventional pressure-based methods were made. Further analysis of the particulate matter loading rates was obtained with a mass-based total PM emission measurement instrument (TEOM) and DPF gravimetric measurements.

Greenhouse gas regulations and global economic growth are expected to drive a future demand shift towards diesel fuel in the transportation sector. This may create a market opportunity for cost-effective fuels in the light distillate range if they can be burned as efficiently and cleanly as diesel fuel. In this study, the emission performance of a low cetane number, low research octane number naphtha (CN 34, RON 56) was examined on a production 6-cylinder heavy-duty on-highway truck engine and aftertreatment system. Using only production hardware, both the engine-out and tailpipe emissions were examined during the heavy-duty emission testing cycles using naphtha and ultra-low-sulfur diesel (ULSD) fuels. Without any modifications to the hardware and software, the tailpipe emissions were comparable when using either naphtha or ULSD on the heavy duty test cycles.

Reducing cold-start emissions to meet increasingly stringent emissions limits requires fast activation of exhaust system sensors and aftertreatment control strategies. One factor delaying the activation time of current exhaust sensors, such as NOx and particulate matter (PM) sensors, is the need to protect these sensors from water present in the exhaust system. Exposure of the ceramic sensing element to water droplets can lead to thermal shock and failure of the sensor. In order to prevent such failures, various algorithms are employed to estimate the dew point of the exhaust gas and determine when the exhaust system is sufficiently dry to enable safe sensor operation. In contrast to these indirect, model-based approaches, this study utilized radio frequency (RF) sensors typically applied to monitor soot loading levels in diesel and gasoline particulate filters, to provide a direct measurement of stored water levels on the ceramic filter elements themselves.

Reliable means for on-board detection of particulate filter failures or malfunctions are needed to meet diagnostics (OBD) requirements. Detecting these failures, which result in tailpipe particulate matter (PM) emissions exceeding the OBD limit, over all operating conditions is challenging. Current approaches employ differential pressure sensors and downstream PM sensors, in combination with particulate filter and engine-out soot models. These conventional monitors typically operate over narrowly-defined time windows and do not provide a direct measure of the filter’s state of health. In contrast, radio frequency (RF) sensors, which transmit a wireless signal through the filter substrate provide a direct means for interrogating the condition of the filter itself.

In this study, the compression ratio of a commercial 15L heavy-duty diesel engine was lowered and a split injection strategy was developed to promote partially premixed compression ignition (PPCI) combustion. Various low reactivity gasoline-range fuels were compared with ultra-low-sulfur diesel fuel (ULSD) for steady-state engine performance and emissions. Specially, particulate matter (PM) emissions were examined for their mass, size and number concentrations, and further characterized by organic/elemental carbon analysis, chemical speciation and thermogravimetric analysis. As more fuel-efficient PPCI combustion was promoted, a slight reduction in fuel consumption was observed for all gasoline-range fuels, which also had higher heating values than ULSD. Since mixing-controlled combustion dominated the latter part of the combustion process, hydrocarbon (HC) and carbon monoxide (CO) emissions were only slightly increased with the gasoline-range fuels.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

Although designed for the purpose of reducing engine-out Particulate Matter (PM) emissions to meet or exceed mandated emissions regulations, the particulate filter also incurs a fuel economy penalty. This fuel penalty is due to the increased exhaust flow restriction attributed to the PM accumulated in the filter, in addition to fuel consumed for active regeneration. Unlike the soot which may be oxidized through the regeneration process, incombustible material or ash continues to build-up in the filter following each regeneration event. Currently pressure- and model-based controls are used to provide an indirect estimate of the loading state of the particulate filter, in order to manage the filter operation and determine when to regenerate the filter. The challenges associated with pressure- and model-based particulate filter control over real-world operating conditions are well-known.