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This paper proposes a framework to perform design optimization of a series PHEV and investigates the impact of using real-world driving inputs on final design. Real-World driving is characterized from a database of naturalistic driving generated in Field Operational Tests. The procedure utilizes Markov chains to generate synthetic drive cycles representative of real-world driving. Subsequently, PHEV optimization is performed in two steps. First the optimal battery and motor sizes to most efficiently achieve a desired All Electric Range (AER) are determined. A synthetic cycle representative of driving over a given range is used for function evaluations. Then, the optimal engine size is obtained by considering fuel economy in the charge sustaining (CS) mode. The higher power/energy demands of real-world cycles lead to PHEV designs with substantially larger batteries and engines than those developed using repetitions of the federal urban cycle (UDDS).

Because of U.S. EPA regulatory actions and the National Academies National Research Council suggestions for improvements in the U.S. EPA emissions inventory methods, the U.S. EPA' Office of Transportation and Air Quality (OTAQ) has made a concerted effort to develop instrumentation that can measure criteria pollutant emissions during the operation of on-road and off-road vehicles. These instruments are now being used in applications ranging from snowmobiles to on-road passenger cars to trans-Pacific container ships. For the betterment of emissions inventory estimation these on-vehicle instruments have recently been employed to measure time resolved (1 hz) in-use gaseous emissions (CO₂, CO, THC, NO ) and particulate matter mass (with teflon membrane filter) emissions from 29 non-road construction vehicles (model years ranging from 1993 to 2007) over a three year period in various counties in Iowa, Missouri, and Kansas.

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

As part of the U.S. Environmental Protection Agency’s (EPA’s) continuing assessment of advanced light-duty automotive technologies in support of regulatory and compliance programs, a 2018 Toyota Camry front wheel drive eight-speed automatic transmission was benchmarked. The benchmarking data were used as inputs to EPA’s Advanced Light-duty Powertrain and Hybrid Analysis (ALPHA) vehicle simulation model to estimate GHG emissions from light-duty vehicles. ALPHA requires both detailed engine fuel consumption maps and transmission torque loss maps. EPA’s National Vehicle and Fuels Emissions Laboratory has developed a streamlined, cost-effective in-house method of transmission testing, capable of gathering a dataset sufficient to characterize transmissions within ALPHA. This testing methodology targets the range of transmission operation observed during vehicle testing over EPA’s city and highway drive cycles.

The Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created by EPA to estimate greenhouse gas (GHG) emissions from light-duty (LD) vehicles [1]. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types combined with different powertrain technologies. The software tool is a MATLAB/Simulink based desktop application. In order to model the behavior of current and future vehicles, an algorithm was developed to dynamically generate transmission shift logic from a set of user-defined parameters, a cost function (e.g., engine fuel consumption) and vehicle performance during simulation. This paper presents ALPHA's shift logic algorithm and compares its predicted shift points to actual shift points from a mid-size light-duty vehicle and to the shift points predicted using a static table-based shift logic as calibrated to the same vehicle during benchmark testing.

This paper addresses scheduling of quantized power levels (including part load operation and startup/shutdown periods) for a propane powered solid oxide fuel cell (SOFC) hybridized with a lithium-ion battery for a tracked mobile robot. The military requires silent operation and long duration missions, which cannot be met by batteries alone due to low energy density or with combustion engines due to noise. To meet this need we consider an SOFC operated at a few discrete power levels where maximum system efficiency can be achieved. The fuel efficiency decreases during transients and resulting thermal gradients lead to stress and degradation of the stack; therefore switching power levels should be minimized. Excess generated energy is used to charge the battery, but when it’s fully charged the SOFC should be turned off to conserve fuel.

The U.S. Environmental Protection Agency’s (EPA’s) Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created to estimate greenhouse gas (GHG) emissions from light-duty vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types with different powertrain technologies, showing realistic vehicle behavior, and auditing of all energy flows in the model. In preparation for the midterm evaluation (MTE) of the 2017-2025 light-duty GHG emissions rule, ALPHA has been refined and revalidated using newly acquired data from model year 2013-2016 engines and vehicles. The robustness of EPA’s vehicle and engine testing for the MTE coupled with further validation of the ALPHA model has highlighted some areas where additional data can be used to add fidelity to the engine model within ALPHA.

This study uses full drive cycle simulation to compare the fuel consumption of a vehicle with a turbocharged (TC) engine to the same vehicle with an alternative boosting technology, namely, a hybrid supercharger, in which a planetary gear mechanism governs the power split to the supercharger between the crankshaft and a 48 V 5 kW electric motor. Conventional mechanically driven superchargers or electric superchargers have been proposed to improve the dynamic response of boosted engines, but their projected fuel efficiency benefit depends heavily on the engine transient response and driver/cycle aggressiveness. The fuel consumption benefits depend on the closed-loop engine responsiveness, the control tuning, and the torque reserve needed for each technology. To perform drive cycle analyses, a control strategy is designed that minimizes the boost reserve and employs high rates of combustion dilution via exhaust gas recirculation (EGR).

A GT-SUITE vehicle-aftertreatment model has been developed to examine the cold-start emissions reduction capabilities of a Vacuum Insulated Catalytic Converter (VICC). This converter features a thermal management system to maintain the catalyst monolith above its light-off temperature between trips so that most of a vehicle’s cold-start exhaust emissions are avoided. The VICC thermal management system uses vacuum insulation around the monoliths. To further boost its heat retention capacity, a metal phase-change material (PCM) is packaged between the monoliths and vacuum insulation. To prevent overheating of the converter during periods of long, heavy engine use, a few grams of metal hydride charged with hydrogen are attached to the hot side of the vacuum insulation. The GT-SUITE model successfully incorporated the transient heat transfer effects of the PCM using the effective heat capacity method.

Reducing the weight of the M1A2 tank by lightweighting hull, suspension, and track results in 5.1%, 1.3%, and 0.6% tank mass reductions, respectively. The impact of retrofitting with lightweight components is evaluated through primary energy demand (PED), cost, and fuel consumption (FC). Life cycle stages included are preproduction (design, prototype, and testing), material production, part fabrication, and operation. Metrics for lightweight components are expressed as ratios comparing lightweighted and unmodified tanks. Army-defined drive cycles were employed and an FC vs. mass elasticity of 0.55 was used. Depending on the distance traveled, cost to retrofit and operate a tank with a lightweighted hull is 3.5 to 19 times the cost for just operating an unmodified tank over the same distance. PED values for the lightweight hull are 1.1 to 2 times the unmodified tank. Cost and PED ratios decrease with increasing distance.

Electrification and hybridization have great potential for improving fuel economy and reducing visual signature or soot emissions in military vehicles. Specific challenges related to military applications include severe duty cycles, large and uncertain energy flows through the system and high thermal loads. A novel supervisory control strategy is proposed to simultaneously mitigate severe engine transients and to reduce high electric current in the battery without oversizing the battery. The described objectives are accomplished by splitting the propulsion power demand through filtering in the frequency domain. The engine covers only low frequency power demand profile while the battery covers high frequency components. In the proposed strategy, the separation filter is systematically designed to identify different frequency components with the consideration of fuel consumption, aggressive engine transients, and battery electric loads.

The increasing numbers of hybrid electric and full electric vehicle models currently in the market or in the pipeline of automotive OEMs require creative testing mechanisms to drive down development costs and optimize the efficiency of these vehicles. In this paper, such a testing mechanism that has been successfully implemented at the US Environmental Protection Agency National Vehicle and Fuel Emissions Laboratory (EPA NVFEL) is described. In this testing scheme, the units-under-test consist of a battery pack and its associated battery management system (BMS). The remaining subsystems, components, and environment of the vehicle are virtual and modeled in high fidelity.

As part of an ongoing assessment of the potential for reducing greenhouse gas (GHG) emissions of light-duty vehicles, the U.S. Environmental Protection Agency (EPA) has implemented an updated methodology for applying the results of full vehicle simulations to the range of vehicles across the entire fleet. The key elements of the updated methodology explored for this article, responsive to stakeholder input on the EPA’s fleet compliance modeling, include (1) greater transparency in the process used to determine technology effectiveness and (2) a more direct incorporation of full vehicle simulation results. This article begins with a summary of the methodology for representing existing technology implementations in the baseline fleet using EPA’s Advanced Light-duty Powertrain and Hybrid Analysis (ALPHA) full vehicle simulation. To characterize future technologies, a full factorial ALPHA simulation of every conventional technology combination to be considered was conducted.

Exposure of a driver to vehicle vibration is known to disrupt manual performances, and more specifically affect the speed and accuracy of reaching tasks associated with vehicle operation. The effects of whole body vibration (WBV) can be analyzed as a function of the vibration characteristics of each body link. This information can then be used to identify movement strategies and predict biodynamic responses. Conceptual principles derived from the understanding of human behavior in a vibratory environment can then be used for the design of controls or interfaces adapted for vehicle operation in this context. The transfer functions of individual upper body links were estimated to investigate their biodynamic properties as a function of vehicle vibration frequency and spatial location of targets to be reached. In the present study, fourteen seated participants performed pointing movements to eight targets distributed in the right hemisphere.

Industrial tasks performed by standing workers are among those most commonly simulated using digital human models. Workers often walk, turn, and take acyclic steps as they perform these tasks. Current h uman modeling tools lack the capability to simulate these whole body motions accurately. Most models simulate walking by replaying joint angle trajectories corresponding to a general gait pattern. Turning is simulated poorly if at all, and violations of kinematic constraints between the feet and ground are common. Moreover, current models do not accurately predict foot placement with respect to loads and other hand targets, diminishing the utility of the associated ergonomic analyses. A new approach to simulating stepping and walking in task-oriented activities is proposed. Foot placements and motions are predicted from operator and task characteristics using empirical models derived from laboratory data and validated using field data from an auto assembly plant.

The pulleys employed in automotive accessory drive systems very often consist of a two piece assembly; a multitude of fastening techniques are used in completing the assembly. There are numerous assembly methods and a variety of distinct pulley configurations dictated by the various automobile manufacturers in accordance with individual accessory drive needs. An expert system is being developed to evaluate the merit of multiple assembly alternatives for a specific pulley application. The expert system provides a consistent evaluation tool for assembly alternatives, balancing the influence of product cost, strength and quality considerations. The knowledge-based system is implemented in an expert system shell called AGNESS (A Generalized Network-based Expert System Shell). The expert system judges the acceptability of various pulley assembly techniques, assigning a high “merit value” to the better designs and proportionately lower values to less desirable designs.

Half of the car occupant deaths involved in two-vehicle crashes results from side impact collisions. In an attempt to better understand the role that vehicle mass plays in crashes and injury causation, detailed information from the NASS CDS database on injury source was distributed in three classes: contact with intrusion, contact without intrusion, and restrained acceleration or non-contact. We compared these distributions for belted drivers in side verses frontal crashes. When looking at the type of striking, or bullet, vehicle in near-side impacts, we found that intrusion injuries are more prevalent in cars hit by SUVs and pickups than by other cars. We also looked at the body region injured verses the type of striking vehicle and found head injuries to be slightly more prevalent when the striking vehicle is an SUV or pick-up. Data from the University of Michigan CIREN case studies on side impacts are presented and are consistent with the NASS CDS data.

While the results are generally the most exciting aspects of an LCI study, the details of the LCI model that generates the results are equally significant; particularly when modeling the life cycle of an automobile. The modeling challenges faced in conducting the US AMP LCI of a mid-sized vehicle based on the 1995 Lumina, Intrepid and Taurus are highlighted. The number of parts (over 20,000), supply chain complexity, materials composition, and the demanding set of OEM requirements for model features required special LCI methods and solutions. The LCI model and selected results are compared with previous studies, and recommendations for improvements in the USAMP LCI model are also provided. This paper is one of six SAE publications discussing the results and execution of the USCAR AMP Generic Vehicle LCI. The papers in this series are (Overview of results 982160, 982161, 982162, 982168, 982169, 982170).

This paper investigates how reaching and grasping are affected by various object properties and conditions. While previous studies have examined the effect of object attributes such as size, shape, and distance from the subject, there is a need for quantitative models of finger motions. To accomplish this, the experiment was performed with six subjects where the 3D-coordinates of the finger joints and the wrist of one hand were recorded during reaching and grasping tasks. Finger joint angles at final posture were found to depend on both object size and orientation while wrist postures were changed primarily depending on object orientation. Also, each object orientation caused alteration in relative object location with respect to the hand at final posture. In addition, analysis of temporal variables revealed that it took from 1.06 to 1.30 seconds depending on the object distance to start reaching and complete grasping of the object.

The Energy Boundary Element Analysis (EBEA) has been utilized in the past for computing the exterior acoustic field at high frequencies (above ∼400Hz) around vehicle structures and numerical results have been compared successfully to test data [1, 2 and 3]. The Energy Finite Element Analysis (EFEA) has been developed for computing the structural vibration of complex structures at high frequencies and validations have been presented in previous publications [4, 5]. In this paper the EBEA is utilized for computing the acoustic field around a vehicle structure due to external acoustic noise sources. The computed exterior acoustic field comprises the excitation for the EFEA analysis. Appropriate loading functions have been developed for representing the exterior acoustic loading in the EFEA simulations, and a formulation has been developed for considering the acoustic treatment applied on the interior side of structural panels.