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All around the world, steps are being taken to improve the quality of our environment. Prominent among these are the definition, implementation, and attainment of increasingly stringent emissions regulations for all types of engines, including off-highway diesels. These rigorous regulations have driven use of technologies like after-treatment, advanced air systems, and advanced fuel systems. Fuel dispensed off-highway is routinely and significantly dirtier than fuel from on-highway outlets. Furthermore, fuels used in developing countries can be up to 30 times dirtier than the average fuels in North America. Poor fuel cleanliness, coupled with the higher pressures and performance demands of modern fuel systems, create life challenges greater than encountered with cleaner fuels. This can result in costly disruption of operations, loss of productivity, and customer dissatisfaction in the off-highway market.

Michigan Technological University has established a broad-based university-wide research initiative, termed Wood-to-Wheels (W2W), to develop and evaluate improved technologies for growing, harvesting, converting, and using woody biomass in renewable transportation fuel applications. The W2W program bridges the entire biomass development-production-consumption life cycle with research in areas including forest resources, bioprocessing, engine/vehicle systems, and sustainable decisions. The W2W chain establishes a closed cycle of carbon between the atmosphere, woody biomass, fuels, and vehicular systems that can reduce the accumulation of CO2 in the atmosphere. This paper will summarize the activities associated with the Wood-to-Wheels initiative and describe challenges and the potential benefits that are achievable.

In this paper, two correlations, Windowed Selected Moving Autocorrelation (WSMA) and Tri-Correlation (TriC), are introduced and discussed. The WSMA is simpler than the conventional autocorrelation. WSMA uses less data points to obtain useful signal content at desired frequencies. The computational requirement is therefore reduced compared to the conventional autocorrelation. The simplified TriC provides improved signal to noise separation capability than WSMA does while still requiring reduced computational effort compared to the standard autocorrelation. Very often, computation resource limitation exists for real-time applications. Therefore, the WSMA and TriC offer more opportunities for real-time monitor and feedback control applications in the frequency domain due to their high efficiencies. As an example, applications in internal combustion (IC) engine misfire detection are presented. Simulation and vehicle test results are also presented in this paper.

This paper reviews the engine modeling program in the Powertrain Control Research Laboratory at the University of Wisconsin-Madison, focuses on simulation results obtained from a complete modular turbocharged diesel engine dynamic model developed in this lab, and suggests ways that dynamic engine system models can be used in the design process. It examines the dynamic responses and interactions between various components in the engine system, looks at how these components affect the overall performance of the system in transient and steady state operation.

Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.

The Kalman and Vold-Kalman order tracking filters have been implemented in commercial software since the early 90's. There are several mathematical formulations of filters that have been implemented by different software vendors. However, there have not been any papers that have been published which sufficiently explain the math behind these filters and discuss the actual implementations of the filters in software. In addition, upon generating the equations represented by these filters, solving the equations for datasets in excess of several hundred thousand datapoints is not trivial and has not been discussed in the literature. The papers which have attempted to cover these topics are generally vague and overly mathematically eloquent but not easily understandable by a practicing engineer.

This paper describes the transient spray characteristics of a high pressure, single fluid injector, intended for use in a direct-injection spark-ignited (DISI) engine. The injector was a single hole, pintle type injector and was electronically controlled. A variety of measurement diagnostics, including full-field imaging and line-of-sight diffraction based particle sizing were employed for spray characterization. Transient patternator measurements were also performed to obtain temporally resolved average mass flux distributions. Particle size and obscuration measurements were performed at three locations in the spray and at three injection pressures: 3.45 MPa (500 psi), 4.83 Mpa (700 psi), and 6.21 MPa (900 psi). Results of the spray imaging experiments indicated that the spray shapes varied with time after the start of injection and contained a leading mass, or slug along the center line of the spray.

The traditional method of dynamic characterization of elastomers used in industry has largely been based on sinusoidal input excitation. Discrete frequency sine wave signals at specified amplitudes are used to excite the elastomer in a step-sine sweep fashion. This paper will examine new methods of characterization using various broadband input excitations. These different inputs include continuous sine sweep (chirp), shaped random, and acquired road profile data. Use of these broadband data types is expected to provide a more accurate representation of conditions seen in the field, while helping to eliminate much of the interpolation that is inherent with the classic discrete step-sine technique. Results of the various input types are compared in this paper with those found using the classic discrete step-sine input.

The automotive industry is dramatically changing. Many automotive Original Equipment Manufacturers (OEMs) proposed new prototype models or concept vehicles to promote a green vehicle image. Non-traditional players bring many latest technologies in the Information Technology (IT) industry to the automotive industry. Typical vehicle’s characteristics became wider compared to those of vehicles a decade ago, and they include not only a driving range, mileage per gallon and acceleration rating, but also many features adopted in the IT industry, such as usability, connectivity, vehicle software upgrade capability and backward compatibility. Consumers expect the latest technology features in vehicles as they enjoy in using digital applications in laptops and mobile phones. These features create a huge challenge for a design of a new vehicle, especially for a human-machine-interface (HMI) system.

China has become the world’s largest vehicle market in terms of sales volume. Automobiles sales keep growing in recent years despite the declining economic growth rate. Due to the increasing attention given to the environmental impact, more stringent emission regulations are being drafted to control traditional internal combustion engine emissions. In order to reduce vehicle emissions, environmentally-friendly new-energy vehicles, such as electric vehicles and plug-in hybrid vehicles, are being promoted by government policies. The Chinese government plans to boost sales of new-energy cars to account for about five percent of China’s total vehicle sales. It is well known that more electric and electronic components will be integrated into a vehicle platform during vehicle electrification.

Software systems on electronically controlled diesel truck engines typically provide diagnostic features to enable the engine mechanic to identify and debug system problems. As future systems become more sophisticated, so will the diagnostic requirements. The advantages of serviceability and accuracy found in todays electronic systems must not be allowed to degrade due to this increased sophistication. One method of maintaining a high level of serviceability and accuracy is to place an even greater priority on diagnostics and servicing in the initial design phase of the product than is done today. In particular, three major goals of future diagnostic systems should be separation of component failures from system failures, prognostication of failures and analysis of engine performance. This paper will discuss a system to realize these goals by dividing the diagnostic task into the Electronic System Diagnostics, Engine System Diagnostics and the Diagnostic Interface.

The role of mixture stratification on combustion rate has been investigated in a constant volume combustion vessel in which mixtures of different equivalence ratios can be added in a spatially and temporally controlled fashion. The experiments were performed in a regime of low fluid motion to avoid the complicating effects of turbulence generated by the injection of different masses of fluid. Different mixture combinations were investigated while maintaining a constant overall equivalence ratio and initial pressure. The results indicate that the highest combustion rate for an overall lean mixture is obtained when all of the fuel is contained in a stoichiometric mixture in the vicinity of the ignition source. This is the result of the high burning velocity of these mixtures, and the complete oxidation which releases the full chemical energy.

Off-highway machine mounting system isolation, especially the cab mounting system, significantly affects the operator comfort by providing damping to the harsh inputs and isolating the structure-borne energy from traveling into the cab. Mounting system isolation performance is decided not only by the isolation component, but also the mounting bracket structure, and should be treated as a system. This paper gives a review of how the mounting system isolates structural energy and the effect of the bracket structure stiffness to the mounting system isolation performance.

Simulation is a useful tool which can significantly reduce resources invested during product development. Vehicle manufacturers are using simulations to aid in the evaluation of designs and components, including powertrain systems and controllers. These simulations can be made more useful by addressing issues such as flexibility, modularity, and causality. These issues and other aspects involved in the development and use of powertrain system simulations are discussed in this paper, and a case study of a powertrain system model developed in the PCRL using methodologies based on considerations of such issues is presented.

The application of Artificial Intelligence (AI) in the automotive industry can dramatically reshape the industry. In past decades, many Original Equipment Manufacturers (OEMs) applied neural network and pattern recognition technologies to powertrain calibration, emission prediction and virtual sensor development. The AI application is mostly focused on reducing product development and validation cost. AI technologies in these applications demonstrate certain cost-saving benefits, but are far from disruptive. A disruptive impact can be realized when AI applications finally bring cost-saving benefits directly to end users (e.g., automation of a vehicle or machine operation could dramatically improve the efficiency). However, there is still a gap between current technologies and those that can fully give a vehicle or machine intelligence, including reasoning, knowledge, planning and self-learning.

Off road navigation demands ground robots to traverse complex and often changing terrain. Classification and assessment of terrain can improve path planning strategies by reducing travel time and energy consumption. In this paper we introduce a terrain classification and assessment framework that relies on both exteroceptive and proprioceptive sensor modalities. The robot captures an image of the terrain it is about to traverse and records corresponding vibration data during traversal. These images are manually labelled and used to train a support vector machine (SVM) in an offline training phase. Images have been captured under different lighting conditions and across multiple locations to achieve diversity and robustness to the model. Acceleration data is used to calculate statistical features that capture the roughness of the terrain whereas angular velocities are used to calculate roll and pitch angles experienced by the robot.

Several very different order tracking and analysis techniques for rotating equipment have been developed recently that are available in commercial noise and vibrations software packages. Each of these order tracking methods has distinct trade-offs for many common applications and very specific advantages for special applications in sound quality or noise and vibrations troubleshooting. The Kalman, Vold-Kalman, Computed Order Tracking, and the Time Variant Discrete Fourier Transform as well as common FFT based order analysis methods will all be presented. The strengths and weaknesses of each of the methods will be presented as well as the highlights of their mathematical properties. This paper is intended to be an overview of currently available technology with all methods presented in a common format that allows easy comparison of their properties. Several analytical examples will be presented to thoroughly document each methods' behavior with different types of data.

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.

There has been an ongoing interest in replacing mineral oil with more biodegradable and/or fire-resistant hydraulic fluids in many mobile equipment applications. Although many alternative fluids may be more biodegradable, or fire-resistant, or both than mineral oil, they often suffer from other limitations such as poorer wear, oxidative stability, and yellow metal corrosion which inhibit their performance in high-pressure hydraulic systems, particularly high pressure piston pump applications. From the fluid supplier's viewpoint, the development of a definitive test, or series of tests, that provides sufficient information to determine how a given fluid would perform with various hydraulic components would be of interest because it would minimize extensive testing. This is often too slow or prohibitively expensive. Furthermore, from OEM's (original equipment manufacturer's) point of view, it would be advantageous to develop a more effective, industry accepted fluid analysis screening.

This paper aims to provide the experimental and numerical investigation of a single fuel droplet impingement on the different wall conditions to understand the detailed impinging dynamic process. The experimental work was carried out at the room temperature and pressure except for the variation of the impinged wall temperature. A high-speed camera was employed to capture the silhouette of the droplet impinging on wall process against a collimated light. Water, diesel, n-dodecane, and n-heptane were considered as four different droplets and injected from a precision syringe pump with the volume flow rate of 0.2 mL/min at various impact Weber numbers. The impingement outcomes after droplet impacting on the wall include stick, spread, rebound and splash, which depend on the controlling parameters of Weber number, Reynolds number, liquid and surface properties, etc.