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This paper presents a comprehensive analysis of emerging powertrain technologies for a wide spectrum of vehicles, ranging from light-duty passenger vehicles to medium and heavy-duty trucks. The study focuses on the anticipated evolution of these technologies over the coming decades, assessing their potential benefits and impact on sustainability. The analysis encompasses simulations across a wide range of vehicle classes, including compact, midsize, small SUVs, midsize SUVs, and pickups, as well as various truck types, such as class 4 step vans, class 6 box trucks, and class 8 regional and long-haul trucks. It evaluates key performance metrics, including fuel consumption, estimated purchase price, and total cost of ownership, for these vehicles equipped with advanced powertrain technologies such as mild hybrid, full hybrid, plug-in hybrid, battery electric, and fuel cell powertrains.

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.

Over the past couple of years, Argonne National Laboratory has tested, analyzed, and validated automobile models for the light duty vehicle class, including several types of powertrains including conventional, hybrid electric, plug-in hybrid electric and battery electric vehicles. Argonne’s previous works focused on the light duty vehicle models, but no work has been done on medium and heavy-duty vehicles. This study focuses on the validation of shifting control in advanced automatic transmission technologies for medium duty vehicles by using Argonne’s model-based high-fidelity, forward-looking, vehicle simulation tool, Autonomie. Different medium duty vehicles, from Argonne’s own fleet, including the Ram 2500, Ford F-250 and Ford F-350, were tested with the equipment for OBD (on-board diagnostics) signal data record. For the medium duty vehicles, a workflow process was used to import test data.

Considerable effort is currently being focused on emerging vehicle automation technologies. Engineers are making great strides in improving safety and reliability, but they are also exploring how these new technologies can enhance energy efficiency. This study focuses on the changes in aerodynamic drag associated with coordinated driving scenarios, also known as “platooning.” To draw sound conclusions in simulation or experimental studies where vehicle speed and gaps are controlled and coordinated, it is necessary to have a robust quantitative understanding of the road load changes associated with each vehicle in the platoon. Many variables affect the drag of each vehicle, such as each gap length, vehicle type/size, vehicle order and number of vehicles in the platoon. The effect is generally understood, but there are limited supporting data in the literature from actual test vehicles driving in formation.

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance.

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.

Improvements in vehicle technology impact the purchase price of a vehicle and its operating cost. In this study, the monetary benefit of a technology improvement includes the potential reduction in vehicle price from using cheaper or smaller components, as well as the discounted value of the fuel cost savings. As technology progresses over time, the value and benefit of improving technology varies as well. In this study, the value of improving a few selected technologies (battery energy density, electric drive efficiency, tire rolling resistance, aerodynamics, light weighting) is studied and the value of the associated cost saving is quantified. The change in saving as a function of time, powertrain selection and vehicle type is also quantified. For example, a 10% reduction in aerodynamic losses is worth $24,222 today but only $8,810 in 2030 in an electric long haul truck. The decrease in value is primarily due to expected battery cost reduction over time.

The objective of this study was to evaluate the fuel saving potential of various hybrid powertrain architectures for medium and heavy duty vehicles. The relative benefit of each powertrain was analyzed, and the observed fuel savings was explained in terms of operational efficiency gains, regenerative braking benefits from powertrain electrification and differences in vehicle curb weight. Vehicles designed for various purposes, namely urban delivery, utility, transit, refuse, drayage, regional and long haul were included in this work. Fuel consumption was measured in regulatory cycles and various real world representative cycles. A diesel-powered conventional powertrain variant was first developed for each case, based on vehicle technical specifications for each type of truck. Autonomie, a simulation tool developed by Argonne National Laboratory, was used for carrying out the vehicle modeling, sizing and fuel economy evaluation.

The peak efficiency of modern spark ignited engines varies from 36% to 40% depending on the exact technology utilized. Most engines can achieve this peak efficiency for a limited operating region. Multi-speed transmissions allow the engine to operate closer to its most efficient operating regions for more significant portions of operation. In the case of hybrid powertrains, electric machines help in improving engine efficiency by adjusting operating speed and load. Engine shutdown during idle events and low loads is another avenue for improving the overall efficiency. The choice of the ideal powertrain and component sizes depends on the engine characteristics, drive cycles and vehicle technical requirements. This study examines what type of powertrains will be suitable for more efficient engines that are likely to be available in the near future. Some of the new technologies achieve higher efficiency with a trade off on power or by accepting a more restrictive operating region.

Diesel-fueled, heavy-duty engines are critical to global economies, but unfortunately they are currently coupled to the rising price and challenging emissions of Diesel fuel. Public awareness and increasingly stringent emissions standards have made Diesel OEMs consider possible alternatives to Diesel, including electrification, fuel cells, and spark ignition. While these technologies will likely find success in certain market segments, there are still many applications that will continue to require the performance and liquid-fueled simplicity of Diesel-style engines. Three-way catalysis represents a possible low-cost and highly-effective pathway to reducing Diesel emissions, but that aftertreatment system has typically been incompatible with Diesel operation due to the prohibitively high levels of soot formation at the required stoichiometric fuel-air ratios. This paper explores a possible method of integrating three-way catalysis with Diesel-style engine operation.

Active transmission warm-up systems are used by automotive manufacturers in effort to increase powertrain efficiency and decrease fuel consumption. These systems vary from one manufacturer to another, but their main goal is to capture waste heat from the powertrain and accelerate transmission fluid warm-up. In this study, the fuel consumption benefit from the active transmission warm-up system in a 2013 Ford Taurus 2.0 L EcoBoost is quantified on a cold start UDDS drive cycle at ambient temperatures of −7 and 21 °C. In addition to this, the fuel consumption and greenhouse gas emissions impact on the EPA 5-cycle test, hot start HWY drive cycle, and a cold start, constant speed drive cycle is also quantified. An extra effort to determine the maximum possible benefit of active transmission warm-up is made by modifying the test vehicle to provide external heating to pre-heat and further accelerate the transmission fluid warm-up.

This work details two approaches for evaluating transmission warming technology: experimental dynamometer testing and development of a simplified transmission efficiency model to quantify effects under varied real world ambient and driving conditions. Two vehicles were used for this investigation: a 2013 Ford Taurus and a highly instrumented 2011 Ford Fusion (Taurus and Fusion). The Taurus included a production transmission warming system and was tested over hot and cold ambient temperatures with the transmission warming system enabled and disabled. A robot driver was used to minimize driver variability and increase repeatability. Additionally the instrumented Fusion was tested cold and with the transmission pre-heated prior to completing the test cycles. These data were used to develop a simplified thermally responsive transmission model to estimate effects of transmission warming in real world conditions.

Mobile source emissions standards are becoming more stringent and particulate emissions from gasoline direct injection (GDI) engines represent a particular challenge. Gasoline particulate filter (GPF) is deemed as one possible technical solution for particulate emissions reduction. In this work, a study was conducted on eight formulations of lubricants to determine their effect on GDI engine particulate emissions and GPF performance. Accelerated ash loading tests were conducted on a 2.4L GDI engine with engine oil injection in gasoline fuel by 2%. The matrix of eight formulations was designed with changing levels of sulfated ash (SASH) level, Zinc dialkyldithiophosphates (ZDDP) level and detergent type. Comprehensive evaluations of particulates included mass, number, size distribution, composition, morphology and soot oxidation properties. GPF performance was assessed through filtration efficiency, back pressure and morphology.

Connectivity and automation are increasingly being developed for cars and trucks, aiming to provide better safety and better driving experience. As these technologies mature and reach higher adoption rates, they will also have an impact on the energy consumption: Connected and Automated Vehicles (CAVs) may drive more smoothly, stop less often, and move at faster speeds, thanks to overall improvements to traffic flows. These potential impacts are not well studied, and any existing studies tend to focus solely on conventional engine-powered cars, leaving aside electrified vehicles such as Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs). This work intends to address this issue by analyzing the energy impact of various CAV scenarios on different types of electric vehicles using high-fidelity models. The vehicles-all midsize, one HEV, one BEV, and a conventional-are modeled in Autonomie, a high-fidelity, forward-looking vehicle simulation tool.

This paper investigates the aerodynamic influence of multiple on-highway trucks in different platooning configurations. Complex pressure fields are generated on the highways due to interference of multiple vehicles. This pressure field causes an aerodynamic drag to be different than the aerodynamic drag of a vehicle in a no-traffic condition. In order to study the effect of platooning, three-dimensional modeling and numerical simulations were performed using STAR-CCM+® commercial Computational Fluid Dynamics (CFD) tool. The aerodynamic characteristics of vehicles were analyzed in five different platooning configurations with two and three vehicles in single and multiple lanes. A significant Yaw Averaged Aerodynamic Drag (YAD) reduction was observed in both leading and trailing vehicles. YAD was based on the average result of three different yaw angles at 0°, −6° and 6°. In single-lane traffic, YAD reduction was up to 8% and 38% in leading and trailing vehicles, respectively.

Near-term advances in spark ignition (SI) engine technology (e.g., variable value lift [VVL], gasoline direct injection [GDI], cylinder deactivation, turbo downsizing) for passenger vehicles hold promise of delivering significant fuel savings for vehicles of the immediate future. Similarly, trends in transmissions indicate higher (8-speed, 9-speed) gear numbers, higher spans, and a focus on downspeeding to improve engine efficiency. Dual-clutch transmissions, which exhibit higher efficiency in lower gears, than the traditional automatics, and are being introduced in the light-duty vehicle segment worldwide. Another development requiring low investment and delivering immediate benefits has been the adaptation of start-stop (micro hybrids or idle engine stop technology) technology in vehicles today.

Manufacturers have been considering various technology options to improve vehicle fuel economy. One of the most cost effective technology is related to advanced transmissions. To evaluate the benefits of transmission technologies and control to support the 2017-2025 CAFE regulations, a study was conducted to simulate many of the many types of transmissions: Automatic transmissions, Manual Transmission as well as Dual Clutch Transmissions including the most commonly used number of gears in each of the technologies (5-speeds, 6-speeds, and 8-speeds). Different vehicle classes were also analyzed in the study process: Compact, Midsize, Small SUV, Midsize SUV and Pickup. This paper will show the fuel displacement benefit of each advanced transmission across vehicle classes.

As a result of increasingly stringent regulations and higher customer expectations, auto manufacturers have been considering numerous technology options to improve vehicle fuel economy. Transmissions have been shown to be one of the most cost-effective technologies for improving fuel economy. Over the past couple of years, transmissions have significantly evolved and impacted both performance and fuel efficiency. This study validates the shifting control of advanced automatic transmission technologies in vehicle systems by using Argonne National Laboratory's model-based vehicle simulation tool, Autonomie. Different midsize vehicles, including several with automatic transmission (6-speeds, 7-speeds, and 8-speeds), were tested at Argonne's Advanced Powertrain Research Facility (APRF). For the vehicles, a novel process was used to import test data.

Detailed characteristics of particulate matter (PM) from a gasoline-direct-injection (GDI) engine were analyzed in terms of primary and aggregate particle sizes, morphology, and nanostructures. For the work, PM was collected from exhaust streams of the engine on transmission electron microscope (TEM) grids by using a thermophoretic sampler. To evaluate the effects of engine load and speed on the properties of PM, the engine was operated at loads of 25, 50, and 75% at 1500 and 3000 rpm. In addition, the effects of fuel injection timing on the PM were examined for samples subjected to injection timings of 190, 230, 260, 300 and 330° bTDC at the constant engine load (50% load) and speed (1500 rpm). The results showed that with advancing injection timing, average primary and aggregate particle sizes gradually increased, which implies that fuel-air mixing is a crucial factor influencing particle size.

The first commercially available Plug-In Hybrid Electric Vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in December 2010. The Volt's powertrain architecture provides four modes of operation, including two that are unique and maximize the Volt's efficiency and performance. The electric transaxle has been specially designed to enable patented operating modes both to improve the electric driving range when operating as a battery electric vehicle and to reduce fuel consumption when extending the range by operating with an internal combustion engine (ICE). However, details on the vehicle control strategy are not widely available because the supervisory control algorithm is proprietary. Since it is not possible to analyze the control without vehicle test data obtained from a well-designed Design-of-Experiment (DoE), a highly instrumented GM Volt, including thermal sensors, was tested at Argonne National Laboratory's Advanced Powertrain Research Facility (APRF).