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Empirical models for engine-out oxides of Nitrogen (NOx) and smoke emissions have been developed for the purpose of minimizing transient emissions while maintaining transient response. Three major issues have been addressed: data acquisition, data processing and modeling method. Real and virtual transient parameters have been identified for acquisition. Accounting for the phase shift between transient engine events and transient emission measurements has been shown to be very important to the quality of model predictions. Several methods have been employed to account for the transient transport delays and sensor lags which constitute the phase shift. Finally several different empirical modeling methods have been used to determine the most suitable modeling method for transient emissions. These modeling methods include several kinds of neural networks, global regression and localized regression.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

One field-returned DPF loaded with a high amount of ash is examined using experimental and modeling approaches. The ash-related design factors are collected by coupling the inspection results from terahertz spectroscopy with a calibrated DPF model. The obtained ash packing density, ash layer permeability and ash distribution profile are then used in the simulation to assess the ash impact on DPF backpressure and regeneration behaviors. The following features have been observed during the simulation: 1 The ash packing density, ash layer permeability and ash distribution profile should be collected at the same time to ensure the accurate prediction of ash impact on DPF backpressure. Missing one ash property could mislead the measurement of the other two parameters and thus affects the DPF backpressure estimation. 2 The ash buildup would gradually increase the frequency for the backpressure-based active soot regeneration.

In this work, an alternative method is proposed and validated for quantifying the axial performance of a state-of-the-art Cu zeolite SCR catalyst. Catalyst cores of a standard length, with varying lengths of wash-coated regions were used to axially resolve the functional performance of the SCR catalyst. This proposed method was validated by quantifying the catalyst entrance and exit effects, as well as the effect of non-uniform wash-coat loading densities. This method is less susceptible to some of the complications highlighted in the previous studies, such as flow uniformity between channels, as well as radiative heating effects, since the product gases are sampled across the entire monolith cross-section rather than through a single catalyst channel. The specific catalyst functions quantified include: NO and NH₃ oxidation, NH₃ storage capacity, as well as NOx conversion efficiency.

Mitigation of ammonia slip from SCR system is critical to meeting the evolving NH₃ emission standards, while achieving maximum NOx conversion efficiency. Ammonia slip catalysts (ASC) are expected to balance high activity, required to oxidize ammonia across a broad range of operating conditions, with high selectivity of converting NH₃ to N₂, thus avoiding such undesirable byproducts as NOx or N₂O. In this work, new insights into the behavior of an advanced ammonia slip catalyst have been developed by using accelerated progressive catalyst aging as a tool for catalyst property interrogation. The overall behavior was deconstructed to several underlying functions, and referenced to an active but non-selective NH₃ oxidation function of a diesel oxidation catalyst (DOC) and to the highly selective but minimally active NH₃ oxidation function of an SCR catalyst.

For renewable fuels to displace petroleum, they must be compatible with emissions control devices. Pure biodiesel contains up to 5 ppm Na + K and 5 ppm Ca + Mg metals, which have the potential to degrade diesel emissions control systems. This study aims to address these concerns, identify deactivation mechanisms, and determine if a lower limit is needed. Accelerated aging of a production exhaust system was conducted on an engine test stand over 1001 h using 20% biodiesel blended into ultra-low sulfur diesel (B20) doped with 14 ppm Na. This Na level is equivalent to exposure to Na at the uppermost expected B100 value in a B20 blend for the system full-useful life. During the study, NOx emissions exceeded the engine certification limit of 0.33 g/bhp-hr before the 435,000-mile requirement.

In this contribution, nuanced changes of a commercial Cu-SSZ-13 catalyst with hydrothermal aging, which have not been previously reported, as well as their corresponding impact on SCR functions, are described. In particular, a sample of Cu-SSZ-13 was progressively aged between 550 to 900°C and the changes of performance in NH3 storage, oxidation functionality and NOx conversion of the catalyst were measured after hydrothermal exposure at each temperature. The catalysts thus aged were further characterized by NH3-TPD, XRD and DRIFTS techniques for structural changes. Based on the corresponding performance and structural characteristics, three different regimes of hydrothermal aging were identified, and tentatively as assigned to “mild”, “severe” and “extreme” aging. Progressive hydrothermal aging up to 750°C decreased NOx conversion to a small degree, as well as NH3 storage and oxidation functions.

In this work fuel dilution of engine oil, and the impact of biodiesel fuel on dilution, were examined. New emissions requirements have driven the adoption of a range of aftertreatment systems for diesel engines. These aftertreatment devices in many cases have specific requirements for exhaust composition and temperature. Meeting these requirements can lead to fuel dilution of the engine oil. Measurement of fuel dilution of engine oil can be challenging, and in this study a new strategy for utilizing Fourier Transform Infrared Spectroscopy (FTIR) was examined. A synthetic component of aviation oil, pentaerythritol ester (PE), was found to be a very useful tracer for measuring dilution with ultra low sulfur diesel (ULSD), but not useful for measuring dilution with B20. Fuel dilution and evaporation rates were measured for both ULSD and for a blend of biodiesel and ULSD (B20).

A first law based regression model for estimating mean value engine torque on-board a diesel engine is presented. The model uses first law terms across the engine control volume in a regression built from least squares to predict engine torque. Torque information is often required by the engine ECM for torque based control and torque broadcast purposes. In the absence of real-time torque measurement torque estimation is usually achieved through look-up tables or empirical models. Given the increase in engine operating parameters as well as engine operating regimes as a result of emission control and exhaust aftertreatment technologies, accurate torque estimation has become more challenging as well as necessary.

Selective Catalytic Reduction (SCR) systems will be widely used to meet the Heavy Duty Diesel (HDD) Euro IV emissions legislation. Reports on a number of demonstrations of such systems have already been published, but the long-term durability of such systems is still to be proven. The potential catalyst deactivation induced by oil-derived species and thermal processes have, up to now, received very little attention, despite the fact that these HDD emission control systems will need to be durable for distances of the order of 500,000 km or more. This paper describes the development and performance of a new family of SCR catalyst with very high thermal durability and poison resistance. The thermal durability of the catalyst was initially demonstrated within long-term, high temperature engine bench ageing studies.

This paper presents an extension of our earlier work on Cummins Vehicle Mission Simulation (VMS) software. Previously, we presented VMS as a Windows based analysis tool to simulate vehicle missions quickly and to gauge, communicate, and improve the value proposition of Cummins engines to customers. We have subsequently extended this VMS architecture to build a grid-computing platform to support high volume of simulation needs. The building block of the grid-computing version of VMS is an executable file that consists of vehicle and engine simulation models compiled using Real Time Workshop. This executable file integrates MATLAB and Simulink with Java, XML, and JDBC technologies and interacts with the MySQL database. Our grid consists of a cluster of twenty Linux servers with quad-core processors. The Sun Grid Engine software suite that administers this cluster can batch-queue and execute 80 simulations concurrently.

Safety, fuel economy and uptime are key requirements for the operation of heavy-duty line-haul trucks within a fleet. With the penetration of connectivity and automation technologies, energy optimal and safe operation of the trucks are further improved through Advanced Driver Assistance System (ADAS) features and automated technologies as in truck platooning. Understanding the braking capability of the vehicle is very important for optimal ADAS and platooning control system design and integration. In this paper, the importance of tire connectivity and tire conditions on truck stopping distance are demonstrated through testing. The test data is further utilized to develop tire models for integration in an optimal vehicle automation for platooning. New ways to produce and use the tire related information in real-time optimal control of platooning trucks are proposed and the contribution of tire information in fuel economy is quantified through simulations.

When developing and designing new technology for integrated vehicle systems deployment, standard cycles have long existed for chassis dynamometer testing and tuning of the powertrain. However, to this day with recent developments and advancements in plug-in hybrid and battery electric vehicle technology, no true “work day” cycles exist with which to tune and measure energy storage control and thermal management systems. To address these issues and in support of development of a range-extended pickup and delivery Class 6 commercial vehicle, researchers at the National Renewable Energy Laboratory in collaboration with Cummins analyzed 78,000 days of operational data captured from more than 260 vehicles operating across the United States to characterize the typical daily performance requirements associated with Class 6 commercial pickup and delivery operation.

A multi-dimensional model of the spark ignition process for SI engines was developed as a user defined function (UDF) integrated into the commercial engine simulation software CONVERGE™ CFD. For the present research, the model simulated spark plasma development in an inert flow environment without combustion. The UT model results were then compared with experiments. The UT CONVERGE CFD-based model includes an electrical circuit sub-model that couples the primary and secondary sides of an inductive ignition system to predict arc voltage and current, from which the transient delivered electrical energy to the gap can be determined. Experimentally measured values of the arc resistance and spark plug calorimeter measurements of the efficiency of electrical to thermal energy conversion in the gap were used to determine the thermal energy delivered to the gas in the spark gap for different pressures and gap distances.

Understanding customer usage space and its impact on engine, after treatment, and vehicle duty cycles poses challenges in terms of data noise, data variability and complex interrelations. Moreover, humans are only able to concurrently visualize at most 2 to 3 dimensions, limiting the number of engine parameters that can be considered. Previous studies in this field have been limited to understanding trends in data based on single duty cycle, comparatively short application period and time domain segmented clustering analysis. These techniques have been used to determine representative cycles for specific applications. In this paper, K-Means Clustering is used to classify customer usage space based on tens of dimensions, for multiple duty cycles, and over years of operation. The clusters are evaluated based on system, sub-system, and component-based metrics on a day based unsegmented engine parameter values.