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The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

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.

The objective of this project, which is supported by the U.S. Department of Energy (DOE) through the National Renewable Energy Laboratory (NREL), is to provide a comprehensive comparison of heavy-duty trucks operating on alternative fuels and diesel fuel. Data collection from up to eight sites is planned. This paper summarizes the design of the project and early results from the first two sites. Data collection is planned for operations, maintenance, truck system descriptions, emissions, duty cycle, safety incidents, and capital costs and operating costs associated with the use of alternative fuels in trucking.

To maximize payback for operators, it is important that natural gas engines for heavy-duty on-road applications minimize fuel consumption. To directly replace a diesel engine for a given vehicle mass and duty cycle, the natural gas engine also needs to match the diesel's power and torque characteristics. This paper reports the results of a development project to increase the torque and power of Westport's 15L 356 kW pilot-ignited, late cycle direct injection of natural gas engine by 10%, while matching or improving efficiency and maintaining emissions compliance. The strategies evaluated to achieve these objectives were to recover some of the exhaust energy with a power turbine, to increase the injector flow area to avoid excessively long combustion durations and to reduce the compression ratio to keep peak cylinder pressure below its maximum limit.

Drivability and powertrain refinement continue to gain importance in the assessment of overall vehicle quality. This notion has transcended its light duty origins and is beginning to gain considerable traction in the medium and heavy duty markets. However, with drivability assessment and refinement also comes the high costs associated with vehicle testing, including items such as test facilities, prototype component evaluation, fuel and human resources. Taking all of this into account, any and all measures must be used to reduce the cost of drivability evaluation and powertrain refinement. This paper describes an analysis based co-simulation methodology, where sophisticated powertrain simulation and objective drivability evaluation tools can be used to predict vehicle drivability. A fast running GT power engine model combined with simplified controls representation in Matlab/Simulink was used to predict engine transients and responses.

In June of 2002, 15 universities participated in the third year of FutureTruck, an advanced vehicle competition sponsored by the U.S. Department of Energy and Ford Motor Company. Using advanced technologies, teams strived to improve vehicle energy efficiency by at least 25%, reduce tailpipe emissions to ULEV levels, and lower greenhouse gas impact of a 2002 Ford Explorer. The competition vehicles were tested for dynamic performance and emissions and were judged in static events to evaluate the design and features of the vehicle. The dynamic events include braking, acceleration, handling, and fuel economy, while the dynamometer testing provided data for both the emissions event and the greenhouse gas event. The vehicles were scored for their performance in each event relative to each other; those scores were summed to determine the winner of the competition. The competition structure included different available fuels and encouraged the use of hybrid electric drivetrains.

The authors present transient wind velocity measurements from two successive, well-documented truck platooning track-test campaigns to assess the wake-shedding behavior experienced by trucks in various platoon formations. Utilizing advanced analytics of data from fast-response (100-200-Hz) multi-hole pressure probes, this analysis examines aerodynamic flow features and their relationship to energy savings during close-following platoon formations. Applying Spectral analysis to the wind velocity signals, we identify the frequency content and vortex-shedding behavior from a forward truck trailer, which dominates the flow field encountered by the downstream trucks. The changes in dominant wake-shedding frequencies correlate with changes to the lead and follower truck fuel savings at short separation distances.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NOx within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions.

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.

A key strategy to improving the real-world fuel consumption and emissions of medium and heavy duty vehicles is the hybridization of these applications. Unlike the passenger vehicle market, medium and heavy duty applications are typically comprised of a range of components from a variety of manufacturers. The vocational market diversity and size places considerable demand on fuel efficiency and emission compliance. Medium and heavy duty applications have the ability to be successfully hybridized in ways that are not currently, or would not be practical within a passenger vehicle. This would also drive greater truck and bus vertical integration of the hybrid components. However, medium and heavy duty manufacturers have been prevented from certifying a full vehicle level platform due to the current engine only certification requirements.

Emissions from 10 refuse trucks equipped with Caterpillar C-10 engines were measured on West Virginia University's (WVU) Transportable Emissions Laboratory in Riverside, California. The engines all used a commercially available Dual-Fuel™ natural gas (DFNG) system supplied by Clean Air Partners Inc. (CAP), and some were also equipped with catalyzed particulate filters (CPFs), also from CAP. The DFNG system introduces natural gas with the intake air and then ignites the gas with a small injection of diesel fuel directly into the cylinder to initiate combustion. Emissions were measured over a modified version of a test cycle (the William H. Martin cycle) previously developed by WVU. The cycle attempts to duplicate a typical curbside refuse collection truck and includes three modes: highway-to-landfill delivery, curbside collection, and compaction. Emissions were compared to similar trucks that used Caterpillar C-10 diesels equipped with Engelhard's DPX catalyzed particulate filters.

Previous studies have shown that regenerating particulate filters are very effective at reducing particulate matter emissions from diesel engines. Some particulate filters are passive devices that can be installed in place of the muffler on both new and older model diesel engines. These passive devices could potentially be used to retrofit large numbers of trucks and buses already in service, to substantially reduce particulate matter emissions. Catalyst-type particulate filters must be used with diesel fuels having low sulfur content to avoid poisoning the catalyst. A project has been launched to evaluate a truck fleet retrofitted with two types of passive particulate filter systems and operating on diesel fuel having ultra-low sulfur content. The objective of this project is to evaluate new particulate filter and fuel technology in service, using a fleet of twenty Class 8 grocery store trucks. This paper summarizes the truck fleet start-up experience.

Vehicles continuously report real-time fuel consumption estimates over their data bus, known as the controller area network (CAN). However, the accuracy of these fueling estimates is uncertain to researchers who collect these data from any given vehicle. To assess the accuracy of these estimates, CAN-reported fuel consumption data are compared against fuel measurements from precise instrumentation. The data analyzed consisted of eight medium/heavy-duty vehicles and two medium-duty engines. Varying discrepancies between CAN fueling rates and the more accurate measurements emerged but without a vehicular trend-for some vehicles the CAN under-reported fuel consumption and for others the CAN over-reported fuel consumption. Furthermore, a qualitative real-time analysis revealed that the operating conditions under which these fueling discrepancies arose varied among vehicles.

To properly compare and contrast the environmental performance of one vehicle technology against another, it is necessary to consider their production, operation, and end-of-life fates. Since 1995, Argonne’s GREET® life cycle analysis model (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) has been annually updated to model and refine the latest developments in fuels and materials production, as well as vehicle operational and composition characteristics. Updated cradle-to-grave life cycle analysis results from the model’s latest release are described for a wide variety of fuel and powertrain options for U.S. light-duty and medium/heavy-duty vehicles. Light-duty vehicles include a passenger car, sports utility vehicle (SUV), and pick-up truck, while medium/heavy-duty vehicles include a Class 6 pickup-and-delivery truck, Class 8 day-cab (regional) truck, and Class 8 sleeper-cab (long-haul) truck.

In the United States, intercity long-haul trucks idle approximately 1,800 hrs per year primarily for sleeper cab hotel loads, consuming 838 million gallons of diesel fuel [1]. The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is working on solutions to this challenge through the CoolCab project. The objective of the CoolCab project is to work closely with industry to design efficient thermal management systems for long-haul trucks that keep the cab comfortable with minimized engine idling. Truck engine idling is primarily done to heat or cool the cab/sleeper, keep the fuel warm in cold weather, and keep the engine warm for cold temperature startup. Reducing the thermal load on the cab/sleeper will decrease air conditioning system requirements, improve efficiency, and help reduce fuel use. To help assess and improve idle reduction solutions, the CoolCalc software tool was developed.

Design of a light duty diesel for an automotive market presents contradictory challenges related to passenger car requirements for a compact, low weight design versus the diesel's base engine that must withstand cylinder pressures that are much greater than that seen on gasoline. This was a particular challenge for Cummins because of two reasons. First, design practices developed for Cummins' traditional heavy duty and industrial markets could lead to over-design, particularly for those items that have wear based life limits like bearings. Secondly, in the pursuit of new engine business it is necessary to be able to quickly yet accurately generate conceptual engine space claims for a variety of vehicle and engine specifications. When applying traditional guidelines for crank and bearing sizing, the resulting base engine size appeared an unsolvable problem relative to size and weight requirements.

A heavy-duty vehicle (HDV) module of the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREETTM) model has been developed at Argonne National Laboratory. The fuel-cycle GREET model has been published extensively and contains data on fuel-cycles and vehicle operation of light-duty vehicles. The addition of the HDV module to the GREET model allows for well-to-wheel (WTW) analyses of heavy-duty advanced technology and alternative fuel vehicles (AFVs), which has been lacking in the literature. WTW analyses of HDVs becomes increasingly important to understand the fuel consumption and greenhouse gas (GHG) emissions impacts of newly enacted and future HDV regulations from the Environmental Protection Agency and the Department of Transportation’s National Highway Traffic Safety Administration.

Currently, connected and autonomous vehicle (CAV) technology is being developed for Class 8 tractor trucks aimed at improved safety and fuel economy and reduced CO2 emissions. Despite extensive efforts conducted across the world, the reported efficiency gains were varied from different research groups, raising concerns about the fidelity of models, the performance of control, and the effectiveness of the experimental validation. One root cause for this variation stems from the fact that the efficiency gain obtained from the CAV is sensitive to real-world conditions, including surrounding traffic and road grade. This study presents an approach aimed at identifying representative public road sections and facilitating CAV research from this perspective. By employing the decision tree regression (DTR) method to the Fleet DNA database, the most representative road sections can be identified.

This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.