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An experimental and computational study was performed to evaluate the performance of the CRT™ technology with an off-highway engine with a cooled low pressure loop EGR system. The MTU-Filter 1D DPF code predicts the particulate mass evolution (deposition and oxidation) in a diesel particulate filter (DPF) during simultaneous loading and during thermal and NO2-assisted regeneration conditions. It also predicts the pressure drop across the DPF, the flow and temperature fields, the solid filtration efficiency and the particle number distribution downstream of the DPF. A DOC model was also used to predict the NO2 upstream of the DPF. The DPF model was calibrated to experimental data at temperatures from 230°C to 550°C, and volumetric flow rates from 9 to 39 actual m3/min.

A 1-D 2-layer model developed previously at MTU was used in this research to predict the pressure drop, filtration characteristics and various properties of the particulate filter and the particulate deposit layer. The model was calibrated and validated for this CPF with data obtained from steady state experiments conducted using a 1995 Cummins M11-330E heavy-duty diesel engine with manual EGR and using ULSF. The CPF used is a NGK filter having a cordierite substrate with NEX catalyst type formulation (54% porosity, 15.0 μm mean pore diameter and 50 gms/ft3 Pt). The filter was catalyzed using a wash coat process. The model was used to predict the pressure drop, particulate mass retained inside the CPF, particulate mass filtration efficiency and concentration downstream of the CPF with agreement between the experimental and simulated data.

A 1-D 2-layer model developed previously at MTU was used in this research to predict the pressure drop, filtration characteristics and various properties of the particulate filter and the particulate deposit layer. The model was used along with dilute emission data to characterize two catalyzed particulate filters (CPFs) having different catalyst loading and catalyst application processes. The model was calibrated and validated with data obtained from steady state experiments conducted using a 1995 Cummins M11-330E heavy-duty diesel engine with manual EGR with different fuels for the two different CPFs. The two different catalyzed particulate filters were CPF III (5 gms/ft3 Pt) and CPF V (50 gms/ft3 Pt). Both the CPFs had cordierite substrates with CPF III and CPF V had MEX and NEX catalyst type formulation respectively. The CPF III filter was catalyzed using a solution-impregnated process while the CPF V filter was catalyzed using a wash coat process.

The effect of high pressure injection (using an accumulator type unit injector with peak injection pressure of approximately 20,000 psi, having a decreasing injection rate profile) on combustion was studied. Combustion results were obtained using a DDA Series 3–53 diesel engine with both conventional analysis techniques and high speed photography. Diesel No. 2 fuel and a low viscosity - high volatility fuel, similar to gasoline were used in the study. Results were compared against baseline data obtained with standard injectors. Some of the characteristics of high pressure injection used with Diesel No. 2 fuel include: substantially improved ignition, shorter ignition delay, and higher pressure rise. Under heavy load - high speed conditions, greater smokemeter readings were achieved with the high pressure injection system with Diesel No. 2 fuel. Higher flame speeds and hence, greater resistance to knock were observed with the high volatility low cetane fuel.

Connected vehicles have the potential to transform the way we commute and travel in a multitude of ways. Vehicles will cooperate and coordinate with each other to solve problems appropriate for the environment in which they are operating. In this paper, we focus on the development of test equipment that includes the infrastructure and vehicles to measure and record all of the information necessary to quantify the performance of cooperative driving algorithms in realistic scenarios. The system allows tests to include real vehicles on the track and virtual vehicles in a digital twin. Real and virtual vehicles interact through the road-side units and test facility network, allowing each test vehicle to receive messages from virtual vehicles as well as the infrastructure. Messages transmitted from the test vehicles are received in the digital twin, allowing the real vehicle to interact with virtual vehicles.

A multiple-injection combustion strategy has been developed for heated gasoline direct injection compression ignition (HGCI). Gasoline was injected into a 0.4L single cylinder engine at a fuel pressure of 300bar. Fuel temperature was increased from 25degC to a temperature of 280degC by means of electric injector heater. This approach has the potential of improving fuel efficiency, reducing harmful CO and UHC as well as particulate emissions, and reducing pressure rise rates. Moreover, the approach has the potential of reducing fuel system cost compared to high pressure (>500bar) gasoline direct injection fuel systems available in the market for GDI SI engines that are used to reduce particulate matter. In this study, a multiple injection strategy was developed using electric heating of the fuel prior to direct fuel injection at engine speed of 1500rpm and load of 12.3bar IMEP.

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques.

A KIVA-based CFD tool has been utilized to simulate the effect of a Common-Rail injection system applied to a large, uniflow-scavenged, two-stroke diesel engine. In particular, predictions for variations of injection pressure and injection duration have been validated with experimental data. The computational models have been evaluated according to their predictive capabilities of the combustion behavior reflected by the pressure and heat release rate history and the effects on nitric oxide formation and wall temperature trends. In general, the predicted trends are in good agreement with the experimental observations, thus demonstrating the potential of CFD as a design tool for the development of large diesel engines equipped with Common-Rail injection. Existing deficiencies are identified and can be explained in terms of model limitations, specifically with respect to the description of turbulence and combustion chemistry.

This paper describes a multi-sensor data set, suitable for testing algorithms to detect and track pedestrians and cyclists, with an autonomous vehicle’s sensor suite. The data set can be used to evaluate the benefit of fused sensing algorithms, and provides ground truth trajectories of pedestrians, cyclists, and other vehicles for objective evaluation of track accuracy. One of the principal bottlenecks for sensing and perception algorithm development is the ability to evaluate tracking algorithms against ground truth data. By ground truth we mean independent knowledge of the position, size, speed, heading, and class of objects of interest in complex operational environments. Our goal was to execute a data collection campaign at an urban test track in which trajectories of moving objects of interest are measured with auxiliary instrumentation, in conjunction with several autonomous vehicles (AV) with a full sensor suite of radar, lidar, and cameras.

The introduction of a thin fluid layer between two layers of sheet metal offers a highly effective and economical alternative to the use of constrained viscoelastic damping layers in sheet metal structures. A diesel engine gear cover, which is constructed of two sheet metal sections spot welded together, takes advantage of fluid layer damping to produce superior vibration and sound radiation performance. In this paper, the bending of a double layered plate coupled through a thin fluid layer is modeled using a traveling wave approach which results in a impedance function that can be used to assess the vibration and sound radiation performance of practical double layered plate structures. Guided by this model, the influence of fluid layer thickness and inside-to-outside sheet thickness is studied.

A model for calculating the trap pressure drop, particulate mass inside the trap and various particulate and trap properties was developed using the steady-state data and the theory developed by Konstandopoulos & Johnson, 1989. Changes were made with respect to the calculation of clean pressure drop, particulate layer porosity and the particulate layer permeability. This model was validated with the data obtained from the steady-state data run with different traps supplied by Corning Inc. The data were collected using the 1988 Cummins L-10 heavy-duty diesel engine using No.2 low sulfur diesel fuel. The three different traps were EX 80 (100 cell density), EX 80 (200 cell density) and EX 66 (100 cell density) all with a 229 mm diameter and 305 mm length. These traps were subjected to different particulate matter loadings at different speeds. The traps were not catalyzed.

When fuel at elevated temperatures is injected into an ambient environment at a pressure lower than the saturation pressure of the fuel, the fuel vaporizes in the nozzle and/or immediately upon exiting the nozzle; that is, it undergoes flash boiling. It is characterized by a two-phase flow regime co-located with primary breakup, which significantly affects the spray characteristics. Under flash boiling conditions, the near nozzle spray angle increases, which can lead to shorter penetration because of increased entrainment. In a multi-hole injector this can cause other impacts downstream resulting from the increased plume to plume interactions. To study the effect of injector temperature and injection pressure with real fuels, an experimental investigation of the spray characteristics of a summer grade gasoline fuel with 10% ethanol (E10) was conducted in an optically accessible constant volume spray vessel.

Engine cylinder pressure is traditionally measured with a piezo-electric pressure transducer, and as such, must be referenced or pegged to a known value. Frequently, the cylinder pressure is pegged to the pressure in the intake manifold plenum whereby the manifold absolute pressure (MAP) at the end of the intake stroke is measured and the cylinder pressure trace for the entire cycle is adjusted such that the cylinder pressure is set equal to the manifold pressure at the end of the intake stroke. However, any error in pegging induces an error in the cylinder pressure trace, which has an adverse effect on the entire combustion analysis. This research is focused on assessing the pegging error for several pegging methods across a wide range of engine operating conditions, and ultimately determining best practices to minimize error in pegging and the calculated combustion metrics. The study was conducted through 1D simulations using the commercially available GT-Power.

This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle using only vehicle speed profile and route elevation as inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon energy optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources.

Diesel combustion and emissions is largely spray and mixing controlled. Spray and combustion models enable characterization over a range of conditions to understand optimum combustion strategies. The validity of models depends on the inputs, including the rate of injection profile of the injector. One method to measure the rate of injection is to measure the momentum, where the injected fuel spray is directed onto a force transducer which provides measurements of momentum flux. From this the mass flow rate is calculated. In this study, the impact of impingement distance, the distance from injector nozzle exit to the anvil connected to the force transducer, is characterized over a range of 2 - 12 mm. This characterization includes the impact of the distance on the momentum flux signal in both magnitude and shape. At longer impingement distances, it is hypothesized that a peak in momentum could occur due to increasing velocity of fuel injected as the pintle fully opens.

This paper reports an experimental and numerical investigation on the spatial and temporal liquid- and vapor-phase distributions of diesel fuel spray under engine-like conditions. The high pressure diesel spray was investigated in an optically-accessible constant volume combustion vessel for studying the influence of the k-factor (0 and 1.5) of a single-hole axial-disposed injector (0.100 mm diameter and 10 L/d ratio). Measurements were carried out by a high-speed imaging system capable of acquiring Mie-scattering and schlieren in a nearly simultaneous fashion mode using a high-speed camera and a pulsed-wave LED system. The time resolved pair of schlieren and Mie-scattering images identifies the instantaneous position of both the vapor and liquid phases of the fuel spray, respectively. The studies were performed at three injection pressures (70, 120, and 180 MPa), 23.9 kg/m3 ambient gas density, and 900 K gas temperature in the vessel.

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller.

An automobile seat’s thermal performance can be challenging to quantify since it requires comprehensive human subject testing. Seat manufacturers must rely on subjective ratings to understand how the construction of a seat and its underlying heating and cooling technology may compare to other seats. Other factors may influence seat ratings published by global marketing information services companies (e.g., JD Power and Associates). In particular, occupants may be biased by the vehicle class in which a seat is installed and by how much the contribution of a specific vehicle’s HVAC system performance affects the perception of seat thermal comfort. Therefore, there is a need for an objective testing methodology that does not rely on human participants but is still capable of producing a thermal performance rating in terms of established thermal comfort scales.

The proliferation of small silicon micro-chips has led to a large assortment of low-cost transducers for data acquisition. Production vehicles on average exploit more than 60 on board sensors, and that number is projected to increase beyond 200 per vehicle by 2020. Such a large increase in sensors is leading the fourth industrial revolution of connectivity and autonomy. One major downfall to installing many sensors is compromises in their accuracy and processing power due to cost limitations for high volume production. The same common errors in data acquisition such as sampling, quantization, and multiplexing on the CAN bus must be accounted for when utilizing an entire array of vehicle sensors. A huge advantage of onboard sensors is the ability to calculate vehicle parameters during a daily drive cycle to update ECU calibration factors in real time. One such parameter is driveline damping, which changes with gear state and drive mode. A damping value is desired for every gear state.

Transmissions have multiple transient events that occur from gear shifting to torque converter clutch application. These transients can be difficult to capture and observe. A six speed front wheeled drive transmission was instrumented with pressure transducers to measure clutches and the torque converter. Due to size restrictions internal to the torque converter the data had to be multiplexed across three different transmitters. A method to capture a transient event through the use of multiplexed data was developed to create a data set with the transient event occurring on each channel. Once testing is completed, the data has to be split into individual channels and synced with the operational data. The data then can be used in both time and frequency domain analysis. It is important to understand that the data is not continuous and must be taken into consideration when post processing it for further results.