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This investigation focuses on conventional powertrain technologies that provide operational synergy based on customer utilization to reduce fuel consumption for a heavy-duty, nonroad (off-road) material handler. The vehicle of interest is a Pettibone Cary-Lift 204i, with a base weight of 50,000 lbs. and a lift capacity of 20,000 lbs. The conventional powertrain consists of a US Tier 4 Final diesel engine, a non-lockup torque converter, a four-speed powershift automatic transmission, and all-wheel drive. The paper will present a base vehicle energy/fuel consumption breakdown of propulsion, hydraulic and idle distribution based on a representative end-user drive cycle. The baseline vehicle test data was then used to develop a correlated lumped parameter model of the vehicle-powertrain-hydraulic system that can be used to explore technology integration that can reduce fuel consumption.

The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

This collection is a resource for studying the history of the evolving technologies that have contributed to snowmobiles becoming cleaner and quieter machines. Papers address design for a snowmobile using the EPA test procedure and standard for off-road vehicles, along with more stringent U.S. National Park Best Available Technology (BAT) standards that are likened to those of the California Air Resourced Board (CARB). Innovative technology solutions include: • Standard application for diesel engine designs • Applications to address and test both engine and track noise • Benefits of the Miller cycle and turbocharging The SAE International Clean Snowmobile Challenge (CSC) program is an engineering design competition. The program provides undergraduate and graduate students the opportunity to enhance their engineering design and project management skills by reengineering a snowmobile to reduce emissions and noise.

This collection is a resource for studying the history of the evolving technologies that have contributed to snowmobiles becoming cleaner and quieter machines. Papers address design for a snowmobile using the EPA test procedure and standard for off-road vehicles. Innovative technology solutions include: • Engine Design: improving the two-stroke, gas direct injection (GDI) engine • Applications of new muffler designs and a catalytic converter • Solving flex-fuel design and engine power problems The SAE International Clean Snowmobile Challenge (CSC) program is an engineering design competition. The program provides undergraduate and graduate students the opportunity to enhance their engineering design and project management skills by reengineering a snowmobile to reduce emissions and noise. The competition includes internal combustion engine categories that address both gasoline and diesel, as well as the zero emissions category in which range and draw bar performance are measured.

This collection is a resource for studying the history of the evolving technologies that have contributed to snowmobiles becoming cleaner and quieter machines. Papers address design for a snowmobile using E10 gasoline (10% ethanol mixed with pump gasoline). Performance technologies that are presented include: • Engine Design: application of the four-stroke engine • Applications to address both engine and track noise • Exhaust After-treatment to reduce emissions The SAE International Clean Snowmobile Challenge (CSC) program is an engineering design competition. The program provides undergraduate and graduate students the opportunity to enhance their engineering design and project management skills by reengineering a snowmobile to reduce emissions and noise. The competition includes internal combustion engine categories that address both gasoline and diesel, as well as the zero emissions category in which range and draw bar performance are measured.

Recognition of Dimethyl Ether (DME) as an alternative fuel has been growing recently due to its fast evaporation and ignition in application of compression-ignition engine. Most importantly, combustion of DME produces almost no particulate matter (PM). The current study provides a further understanding of the combustion process in DME reacting spray via experiment done in a constant volume combustion chamber. Formaldehyde (CH2O), an important intermediate species in hydrocarbon combustion, has received much attention in research due to its unique contribution in chemical pathway that leads to the combustion and emission of fuels. Studies in other literature considered CH2O as a marker for UHC species since it is formed prior to diffusion flame. In this study, the formation of CH2O was highlighted both temporally and spatially through planar laser induced fluorescence (PLIF) imaging at wavelength of 355-nm of an Nd:YAG laser at various time after start of injection (ASOI).

Spray structure has a significant effect on emissions and performance of an internal combustion engine. The main objective of this study is to investigate spray structures based on four different multiple jet impingement injectors. These four different multiple jet-to-jet impingement injectors include 1). 4-hole injector (Case 1), which has symmetric inwardly opening nozzles; 2). 5-1-hole (Case 2); 3). 6-2-hole (Case 3); and 4). 7-3-hole (Case 4) which corresponding to 1, 2, 3 numbers of adjacent holes blocked in a 5-hole, 6-hole, and 7-hole symmetrical drill pattern, respectively. All these configurations are basically 4-holes but with different post collision spray structure. Computational Fluid Dynamics (CFD) work of these sprays has been performed using an Eulerian-Lagrangian modelling approach.

Exergy or availability is the potential of a system to do work. In this paper, an innovative exergy-based control approach is presented for Internal Combustion Engines (ICEs). An exergy model is developed for a Homogeneous Charge Compression Ignition (HCCI) engine. The exergy model is based on quantification of the Second Law of Thermodynamic (SLT) and irreversibilities which are not identified in commonly used First Law of Thermodynamics (FLT) analysis. An experimental data set for 175 different ICE operating conditions is used to construct the SLT efficiency maps. Depending on the application, two different SLT efficiency maps are generated including the applications in which work is the desired output, and the applications where Combined Power and Exhaust Exergy (CPEX) is the desired output. The sources of irreversibility and exergy loss are identified for a single cylinder Ricardo HCCI engine.

Vehicles with power exporting capability are microgrids since they possess electrical power generation, onboard loads, energy storage, and the ability to interconnect. The unique load and silent watch requirements of some military vehicles make them particularly well-suited to augment stationary power grids to increase power resiliency and capability. Connecting multiple vehicles in a peer-to-peer arrangement or to a stationary grid requires scalable power management strategies to accommodate the possibly large numbers of assets. This paper describes a military ground vehicle power management scheme for vehicle-to-grid applications. The particular focus is overall fuel consumption reduction of the mixed asset inventory of military vehicles with diesel generators typically used in small unit outposts.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.

This paper studies model predictive control algorithm for Hybrid Electric Vehicle (HEV) energy management to improve HEV fuel economy. In this paper, Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of power-split HEV. A dedicated model predictive control method is developed to predict vehicle speed, battery state of charge (SOC), and engine fuel consumption. The power output from the engine, motor, and the mechanical brake will be adjusted to match driver's power request at the end of the prediction window while minimizing fuel consumption. The controller model is built on Matlab® MPC toolbox® and the simulations are based on MY04 Prius vehicle model using Autonomie®, a powertrain and fuel economy analysis software, developed by Argonne National Laboratory. The study compares the performance of MPC and conventional rule-base control methods.

Since their inception, the design of airbag sensing systems has continued to evolve. The evolution of air bag sensing system design has been rapid. Electromechanical sensors used in earlier front air bag applications have been replaced by multi-point electronic sensors used to discriminate collision mechanics for potential air bag deployment in front, side and rollover accidents. In addition to multipoint electronic sensors, advanced air bag systems incorporate a variety of state sensors such as seat belt use status, seat track location, and occupant size classification that are taken into consideration by air bag system algorithms and occupant protection deployment strategies. Electronic sensing systems have allowed for the advent of event data recorders (EDRs), which over the past decade, have provided increasingly more information related to air bag deployment events in the field.

Numerous studies describe the fuel consumption benefits of changing the powertrain temperature based on vehicle operating conditions. Actuators such as electric water pumps and active thermostats now provide more flexibility to change powertrain operating temperature than traditional mechanical-only systems did. Various control strategies have been proposed for powertrain temperature set-point regulation. A characteristic of powertrain thermal management systems is that the operating conditions (speed, load etc) change continuously to meet the driver demand and in most cases, the optimal conditions lie on the edge of the constraint envelope. Control strategies for set-point regulation which rely purely on feedback for disturbance rejection, without knowledge of future disturbances, might not provide the full fuel consumption benefits due to the slow thermal inertia of the system.