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Understanding in-use fleet operating behavior is of paramount importance when evaluating the potential of advanced/alternative vehicle technologies. Accurately characterizing real world vehicle operation assists in properly allocating advanced technologies, playing a role in determining initial payback period and return on investment. In addition, this information contributes to the design and deployment of future technologies as the result of increased awareness regarding tractive power requirements associated with typical operating behavior. In this presentation, the concept of vehicle duty cycles and their relation to advanced technologies will be presented and explored. Additionally, current research attempts to characterize school bus operation will be examined, and existing computational analysis and evaluation tools associated with these efforts discussed. Presenter Adam Duran, National Renewable Energy Laboratory

In an effort to characterize the dynamics typical of school bus operation, National Renewable Energy Laboratory (NREL) researchers set out to gather in-use duty cycle data from school bus fleets operating across the country. Employing a combination of Isaac Instruments GPS/CAN data loggers in conjunction with existing onboard telemetric systems resulted in the capture of operating information for more than 200 individual vehicles in three geographically unique domestic locations. In total, over 1,500 individual operational route shifts from Washington, New York, and Colorado were collected. Upon completing the collection of in-use field data using either NREL-installed data acquisition devices or existing onboard telemetry systems, large-scale duty-cycle statistical analyses were performed to examine underlying vehicle dynamics trends within the data and to explore vehicle operation variations between fleet locations.

This study compared fuel economy and emissions between heavy-duty hybrid electric vehicles (HEVs) and equivalent conventional diesel vehicles. In-use field data were collected from daily fleet operations carried out at a FedEx facility in California on six HEV and six conventional 2010 Freightliner M2-106 straight box trucks. Field data collection primarily focused on route assessment and vehicle fuel consumption over a six-month period. Chassis dynamometer testing was also carried out on one conventional vehicle and one HEV to determine differences in fuel consumption and emissions. Route data from the field study was analyzed to determine the selection of dynamometer test cycles. From this analysis, the New York Composite (NYComp), Hybrid Truck Users Forum Class 6 (HTUF 6), and California Air Resource Board (CARB) Heavy Heavy-Duty Diesel Truck (HHDDT) drive cycles were chosen.

Annual fuel use for sleeper cab truck rest period idling is estimated at 667 million gallons in the United States, or 6.8% of long-haul truck fuel use. Truck idling during a rest period represents zero freight efficiency and is largely done to supply accessory power for climate conditioning of the cab. The National Renewable Energy Laboratory’s CoolCab project aims to reduce heating, ventilating, and air conditioning (HVAC) loads and resulting fuel use from rest period idling by working closely with industry to design efficient long-haul truck thermal management systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In addition, if the fuel savings provide a one- to three-year payback period, fleet owners will be economically motivated to incorporate them.

Smart technologies enabling connection among vehicles and between vehicles and infrastructure as well as vehicle automation to assist human operators are receiving significant attention as a means for improving road transportation systems by reducing fuel consumption – and related emissions – while also providing additional benefits through improving overall traffic safety and efficiency. For truck applications, which are currently responsible for nearly three-quarters of the total U.S. freight energy use and greenhouse gas (GHG) emissions, platooning has been identified as an early feature for connected and automated vehicles (CAVs) that could provide significant fuel savings and improved traffic safety and efficiency without radical design or technology changes compared to existing vehicles. A statistical analysis was performed based on a large collection of real-world U.S. truck usage data to estimate the fraction of total miles that are technically suitable for platooning.

In an effort to better understand the operational requirements of port drayage vehicles and their potential for adoption of advanced technologies, National Renewable Energy Laboratory (NREL) researchers collected over 36,000 miles of in-use duty cycle data from 30 Class 8 drayage trucks operating at the Port of Long Beach and Port of Los Angeles in Southern California. These data include 1-Hz global positioning system location and SAE J1939 high-speed controller area network information. Researchers processed the data through NREL’s Drive-Cycle Rapid Investigation, Visualization, and Evaluation tool to examine vehicle kinematic and dynamic patterns across the spectrum of operations. Using the k-medoids clustering method, a repeatable and quantitative process for multi-mode drive cycle segmentation, the analysis led to the creation of multiple drive cycles representing four distinct modes of operation that can be used independently or in combination.

The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL.

The objective of this research project was to compare the emissions of oxides of nitrogen (NOx) from transit buses on as many as five different fuels and three standard transit duty cycles to establish if there is a real-world biodiesel NOx increase for transit bus duty cycles and engine calibrations. Prior studies have shown that B20 can cause a small but significant increase in NOx emissions for some engines and duty cycles. Six buses spanning engine build years 1998 to 2011 were tested on the National Renewable Energy Laboratory's Renewable Fuels and Lubricants research laboratory's heavy-duty chassis dynamometer with certification diesel, certification B20 blend, low aromatic [California Air Resources Board (CARB)] diesel, low aromatic B20 blend, and B100 fuels over the Manhattan, Orange County and UDDS test cycles.

The traditional method of engine knock detection is to compare the knock intensity with a predetermined threshold. The calibration of this threshold is complex and difficult. A statistical knock detection method is proposed in this paper to reduce the effort of calibration. This method dynamically calculates the knock threshold to determine the knock event. Theoretically, this method will not only adapt to different fuels but also cope with engine aging and engine-to-engine variation without re-calibration. This method is demonstrated by modeling and evaluation using real-time engine dynamometer test data.

Today's hybrid electric vehicle (HEV) controls do not necessarily provide maximum fuel savings over all drive cycles. An approach that employs route-based control could improve HEV efficiency at potentially minimal additional cost. This paper evaluates a range of route-based control approaches and identifies look-ahead strategies (using input from “on-the-fly” route predictions) as an area meriting further analysis. A novel implementation approach is developed and discussed, and a comparison with simulation results using an optimized general control setting indicates that fuel savings of approximately 2% to 4% can be obtained with route-based control. Given the increasing prevalence of GPS systems in vehicles, this advance has the potential to provide considerable aggregate fuel savings if applied across the entire national fleet. For instance, a 3% across-the-board reduction in HEV fuel use would save nearly 6.5 million gallons of fuel annually in the United States.

In the absence of prototypes, analytical methods such as finite element analysis are very useful in resolving noise and vibration problems, by predicting dynamic behavior of the automotive components and systems. Finite Element Analysis (FEA) is a simulation technique and involves making assumptions that affect analytical results. Acceptance and use of these results is greatly enhanced through test validation. In this paper, dynamic behavior of the automotive propshaft equipped with cardboard liner and torsional damper is investigated. The finite element model is validated at both component and subsystem levels using frequency response functions. Effects of the cardboard liner and torsional damper on the propshaft bending, torsional and breathing frequencies are studied under free-free boundary conditions. Effects of the U-Joint stiffness along with other design variables on the driveshaft dynamic behavior are also studied.

The fundamentals of multi-leaf spring design as determined through beam theory offers a general perspective on how finite element analysis works. Additionally, the fundamentals of combining dissimilar materials require a basic knowledge of how the combined equivalent modulus affects the overall stiffness characteristics of multi-leaf design. By capturing these basic fundamentals into finite element modeling, an analysis of a steel-composite multi-leaf contact model relative to an idealized steel-composite multi-leaf model shows the importance of contact modeling. The results demonstrate the important differences between an idealized non-contact model relative to a complete contact model.

The starter drive clutch is a one way roller clutch and a key component in a starter motor that is used to crank internal combustion engines. The starter drive clutch transmits torque from an electrical motor to a ring gear mounted on a cranking shaft in an engine thus cranks the engine. The clutch also prevents the whole starter from damage caused by extremely high load and/or extremely high speed applied to the starter pinion from the engine. Drive slippage and barrel cracking are two major failure modes for the starter drive[1]. Insufficient torque capacity results in drive slippage while excessive high hoop stress on the clutch barrel ring causes barrel crack. To eliminate drive slippage failure, the clutch should be designed with high torque capacity. High torque capacity, however, is a cause of high hoop stress on the barrel that may result in the cracked barrel failure. Higher torque capacity and lower hoop stress are two completely opposite design directions.

Interior compartment doors are required by Federal Motor Vehicle Safety Standard (FMVSS) 201, to stay closed during physical head impact testing, and when subjected to specific inertia loads. This paper defines interior compartment doors, and shows examples of several different latches designed to keep these doors closed. It also explores the details of the requirements that interior compartment doors and their latches must meet, including differing requirements from automobile manufacturers. It then shows the conventional static method a supplier uses to analyze a latch and door system. And, since static calculations can't always capture the complexities of a dynamic event, this paper also presents a case study of one particular latch and door system showing a way to simulate the forces experienced by a latch. The dynamic simulation is done using Finite Element Analysis and instrumentation of actual hardware in physical tests.

Visteon has developed a CAE procedure to qualify instrument panel (IP) products under the vehicle key life test environments, by employing a set of CAE simulation and durability techniques. The virtual key life test method simulates the same structural configuration and the proving ground road loads as in the physical test. A representative dynamic road load profile model is constructed based on the vehicle proving ground field data. The dynamic stress simulation is realized by employing the finite element transient analysis. The durability evaluation is based on the dynamic stress results and the material fatigue properties of each component. The procedure has helped the IP engineering team to identify and correct potential durability problems at earlier design stage without a prototype. It has shown that the CAE virtual key life test procedure provides a way to speed up IP product development, to minimize prototypes and costs.

Low resolution fractional factorial experimental designs, used in screening, are more popular than ever due to the ever increasing costs of materials and machine time. Experimenters have to be more precise in their analysis, making every degree of freedom count. Resolution III designs are becoming more commonplace for use in screening designs. When running unsaturated resolution III designs there are extra degrees of freedom stemming from unassigned interactions. It is common practice to utilize these extra degrees of freedom to approximate error. In many cases, this common practice can over state the error and lead to erroneous results regarding factor statistical significance. Utilizing saturated resolution III designs and statistically analyzing unassigned interactions while estimating the error with replication is a method for strengthening the DOE strategy and improving the results from screening designs.

There is still much controversy and confusion in industry today regarding the use of process capability indices and analysis. A lack of knowledge regarding the underlying variation assumptions and rationale sampling strategies in indices such as Cp, Cpk, Pp and Ppk have added to the confusion and in many cases has led to misapplication of these widely used metrics. This issue has also promoted inconsistency in the assessment of long-term versus short-term capability and has hampered the true characterization of processes which is a critical step in any continuous improvement effort. Capability indices and analysis, when properly applied can impart a wealth of knowledge regarding process performance as well as provide focus for improvement activity through the proper characterization and enumeration of variation. Being able to properly characterize variation if the first step in reducing it.

The purpose of this study is to develop specifically a correlation between Exhaust Manifold Cracking Laboratory Test results and 150,000 mile customer fleet usage test results. The study shows that the exhaust manifold design meets the reliability requirements of 10 years or 150,000 miles, given 90th percentile customer usage without an evidence of cracking or audible leaks. This correlation between the Lab Test and the customer Fleet results has been expressed as an acceleration factor. An acceleration factor is the ratio of how much quicker the engine dynamometer test ( i.e. Lab Test ) can accumulate the effect of customer usage over time versus the customers themselves. The acceleration factor is provided for useful life time period of 10 years or 150,000 miles. The recommended acceleration factor, determined in this study, is 38 to 1, comparing the engine dynamometer test ( i.e. Lab Test ) results to 150,000 mile modular truck customer fleet field results.

Realizing the value of knowledge, corporations are turning to Knowledge Based Engineering (KBE) as a design process. A fuel rail KBE tool was created at Visteon with the purpose of increasing knowledge retention and delivering knowledge based designs to the customer much quicker than with conventional methods. Currently, both engineers and CAD designers are using the Fuel Rail KBE Modeler at Visteon. It has been used on many vehicle programs and has saved the company countless person-hours of development time. The Fuel Rail KBE Modeler is a powerful tool that saves resources through automation of both the design and analysis processes. This paper documents the incorporation of automated FEA capability into the KBE environment.

A new risk management method, the System Integrated Life Cycle Risk Management Methodology (SILC RMM), is based on systems engineering principles and is compatible with current standards. The SILC method, created by automotive engineers, addresses shortcomings with FMEA and other risk management (RM) methods, and integrates the FMEA and risk management functions into day-to-day engineering project activities. The SILC approach accommodates technology, cost, schedule, environmental and safety risks throughout the systems engineering project life cycle - from conception to recycle. It allows direct integration of RM information with system and project information for more efficient and effective utilization of resources and optimal overall risk management.