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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.

Striving for sustainable transportation solutions, hydrogen is often identified as a promising energy carrier and internal combustion engines are seen as a cost effective consumer of hydrogen to facilitate the development of a large-scale hydrogen infrastructure. Driven by efficiency and emissions targets defined by the U.S. Department of Energy, a research team at Argonne National Laboratory has worked on optimizing a spark-ignited direct injection engine for hydrogen. Using direct injection improves volumetric efficiency and provides the opportunity to properly stratify the fuel-air mixture in-cylinder. Collaborative 3D-CFD and experimental efforts have focused on optimizing the mixture stratification and have demonstrated the potential for high engine efficiency with low NOx emissions. Performance of the hydrogen engine is evaluated in this paper over a speed range from 1000 to 3000 RPM and a load range from 1.7 to 14.3 bar BMEP.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

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.

The Toyota Prius Prime is a new generation of Toyota Prius plug-in hybrid electric vehicle, the electric drive range of which is 25 miles. This version is improved from the previous version by the addition of a one-way clutch between the engine and the planetary gear-set, which enables the generator to add electric propulsive force. The vehicle was analyzed, developed and validated based on test data from Argonne National Laboratory’s Advanced Powertrain Research Facility, where chassis dynamometer set temperature can be controlled in a thermal chamber. First, we analyzed and developed components such as engine, battery, motors, wheels and chassis, including thermal aspects based on test data. By developing models considering thermal aspects, it is possible to simulate the vehicle driving not only in normal temperatures but also in hot, cold, or warmed-up conditions.

The Battery Performance and Cost Model (BatPaC), developed by Argonne National Laboratory, is a versatile tool designed for lithium-ion battery (LIB) pack engineering. It accommodates user-defined specifications, generating detailed bill-of-materials calculations and insights into cell dimensions and pack characteristics. Pre-loaded with default data sets, BatPaC aids in estimating production costs for battery packs produced at scale (5 to 50 GWh annually). Acknowledging inherent uncertainties in parameters, the tool remains accessible and valuable for designers and engineers. BatPaC plays a crucial role in National Highway Transportation Traffic Safety Administration (NHTSA) regulatory assessments, providing estimated battery pack manufacturing costs and weight metrics for electric vehicles. Integrated with Argonne's Autonomie simulations, BatPaC streamlines large-scale processes, replacing traditional models with lookup tables.

Hydrogen is widely considered a promising fuel for future transportation applications for both, internal combustion engines and fuel cells. Due to their advanced stage of development and immediate availability hydrogen combustion engines could act as a bridging technology towards a wide-spread hydrogen infrastructure. Although fuel cell vehicles are expected to surpass hydrogen combustion engine vehicles in terms of efficiency, the difference in efficiency might not be as significant as widely anticipated [1]. Hydrogen combustion engines have been shown capable of achieving efficiencies of up to 45 % [2]. One of the remaining challenges is the reduction of nitric oxide emissions while achieving peak engine efficiencies. This paper summarizes research work performed on a single-cylinder hydrogen direct injection engine at Argonne National Laboratory.

Building a vehicle model with sufficient accuracy for fuel economy analysis is a time-consuming process, even with the modern-day simulation tools. Obtaining the right kind of data for modeling a vehicle can itself be challenging, given that while OEMs advertise the power and torque capability of their engines, the efficiency data for the components or the control algorithms are not usually made available for independent verification. The U.S. Department of Energy (DOE) funds the testing of vehicles at Argonne National Laboratory, and the test data are publicly available. Argonne is also the premier DOE laboratory for the modeling and simulation of vehicles. By combining the resources and expertise with available data, a process has been created to automatically develop a model for any conventional vehicle that is tested at Argonne. This paper explains the process of analyzing the publicly available test data and computing the parameters of various components from the analysis.

The Prius - a power-split hybrid electric vehicle from Toyota - has become synonymous with the word “Hybrid.” As of October 2010, two million of these vehicles had been sold worldwide, including one million vehicles purchased in the United States. In 2004, the second generation of the vehicle, the Prius MY04, enhanced the performance of the components with advanced technologies, such as a new magnetic array in the rotors. However, the third generation of the vehicle, the Prius MY10, features a remarkable change of the configuration - an additional reduction gear has been added between the motor and the output of the transmission [1]. In addition, a change in the energy management strategy has been found by analyzing the results of a number of tests performed at Argonne National Laboratory's Advanced Powertrain Research Facility (ARRF).

Alternative electric motor geometry with potentially increased efficiency is being considered for hybrid electric vehicle applications. An axial flux motor with a dynamically adjustable air gap (i.e., mechanical field weakening) has been tested, analyzed, and modeled for use in a vehicle simulation tool at Argonne National Laboratory. The advantage of adjusting the flux is that the motor torque-speed characteristics can better match the vehicle load. The challenge in implementing an electric machine with these qualities is to develop a control strategy that takes advantage of the available efficiency improvements without using excessive energy to mechanically adjust the air gap and thus reduce the potential energy savings. Motor efficiency was mapped in terms of speed, torque, supply voltage, and rotor-to-stator air gap.

The U.S. Renewable Fuel Standard (RFS2) requires an increase in the use of advanced biofuels up to 36 billion gallons by 2022. Longer chain alcohols, in addition to cellulosic ethanol and synthetic biofuels, could be used to meet this demand while adhering to the RFS2 corn-based ethanol limitation. Higher carbon number alcohols can be utilized to improve the energy content, knock resistance, and/or petroleum displacement of gasoline-alcohol blends compared to traditional ethanol blends such as E10 while maintaining desired and regulated fuel properties. Part I of this paper focuses on the development of scenarios by which to compare higher alcohol fuel blends to traditional ethanol blends. It also details the implementation of fuel property prediction methods adapted from literature. Possible combinations of eight alcohols mixed with a gasoline blendstock were calculated and the properties of the theoretical fuel blends were predicted.

Higher carbon number alcohols offer an opportunity to meet the Renewable Fuel Standard (RFS2) and improve the energy content, petroleum displacement, and/or knock resistance of gasoline-alcohol blends from traditional ethanol blends such as E10 while maintaining desired and regulated fuel properties. Part II of this paper builds upon the alcohol selection, fuel implementation scenarios, criteria target values, and property prediction methodologies detailed in Part I. For each scenario, optimization schemes include maximizing energy content, knock resistance, or petroleum displacement. Optimum blend composition is very sensitive to energy content, knock resistance, vapor pressure, and oxygen content criteria target values. Iso-propanol is favored in both scenarios' suitable blends because of its high RON value.

Using natural gas in an internal combustion engine (ICE) is emerging as a promising way to improve thermal efficiency and reduce exhaust emissions. In the development of such engine platforms, computational fluid dynamics (CFD) plays a fundamental role in the optimization of geometries and operating parameters. One of the most relevant issues in the simulation of direct injection (DI) gaseous processes is the accurate prediction of the gas jet evolution. The simulation of the injection process for a gaseous fuel does not require complex modeling, nevertheless properly describing high-pressure gas jets remains a challenging task. At the exit of the nozzle, the injected gas is under-expanded, the flow becomes supersonic and shocks occur due to compressibility effects. These phenomena lead to challenging computational requirements resulting from high grid resolution and low computational time-steps.

Early in 2007 President Bush announced in his State of the Union Address a plan to off-set 20% of gasoline with alternative fuels in the next ten years. Ethanol, due to its excellent fuel properties for example, high octane number, renewable character, etc., appears to be a favorable alternative fuel from an engine perspective. Replacing gasoline with ethanol without any additional measures results in unacceptable disadvantages mainly in terms of vehicle range. This paper summarizes combustion studies performed with gasoline as well as blends of gasoline and ethanol. These tests were performed on a modern, 4-cylinder spark ignition engine with direct fuel injection and exhaust gas recirculation. To evaluate the influence of blending on the combustion behavior the engine was operated on the base gasoline calibration. Cylinder pressure data taken during the testing allowed for detailed analysis of rates of heat release and combustion stability.

The CFR F1 engine is the standard testing apparatus used for rating the research octane number (RON) of gasoline fuels. Unlike the motor octane number (MON) method, where the intake port temperature after the carburetor is controlled by an electric heater, the mixture temperature can vary during the RON test due to the heat of vaporization (HoV) of the fuel. Ethanol is receiving increasing attention as a high octane and high HoV fuel component. This work presents an analysis of the combustion characteristics during the RON rating of ethanol fuel blends according to the standard ASTM D2699 method, highlighting the effects of ethanol concentration and base fuel composition. All fuels were blended to a constant RON of 98. Ethanol levels varied from 0 to 50 vol% and the base fuels were surrogate blends composed of primary reference fuels (PRF), toluene standardization fuels (TSF), and a four component gasoline surrogate.

The energy density and power density comparison of conventional fuels and batteries is often mentioned as an advantage of conventional vehicles over electric vehicles. Such an analysis often shows that the batteries are at least an order of magnitude behind fuels like gasoline. However this incomplete analysis ignores the impact of powertrain efficiency and mass of the powertrain itself. When we compare the potential of battery electric vehicles (BEVs) as an alternative for conventional vehicles, it is important to include the energy in the fuel and their storage as well as the eventual conversion to mechanical energy. For instance, useful work expected out of a conventional vehicle as well as a BEV is the same (to drive 300 miles with a payload of about 300 lb). However, the test weight of a Conventional vehicle and BEV will differ on the basis of what is needed to convert their respective stored energy to mechanical energy.

The first commercially available plug-in hybrid electric vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in mid-December 2010. The Volt uses a series-split powertrain architecture, which provides benefits over the series architecture that typically has been considered for use in electric-range extended vehicles (EREVs). A specialized EREV powertrain, called the Voltec, drives the Volt through its entire range of speed and acceleration with battery power alone and within the limit of battery energy, thereby displacing more fuel with electricity than a PHEV, which characteristically blends electric and engine power together during driving. This paper assesses the benefits and drawbacks of these two different plug-in hybrid electric architectures (series versus series-split) by comparing component sizes, system efficiency, and fuel consumption over urban and highway drive cycles.

With the success of hybrid electric vehicles (HEVs) and the still uncertain long-term solution for vehicle transportation, Plug-in Hybrid Electric Vehicles (PHEV) appear to be a viable short-term solution and are of increasing interest to car manufacturers. Like HEVs, PHEVs offer two power sources that are able to independently propel the vehicle. They also offer additional electrical energy onboard. In addition to choices about the size of components for PHEVs, choices about powertrain configuration must be made. In this paper, we consider three potential architectures for PHEVs for 10- and 40-mi All Electric Range (AER) and define the components and their respective sizes to meet the same set of performance requirements. The vehicle and component efficiencies in electric-only and charge-sustaining modes will be assessed.

Dual-fuel combustion using port-injection of low reactivity fuel combined with direct injection of a higher reactivity fuel, otherwise known as Reactivity Controlled Compression Ignition (RCCI), has been shown as a method to achieve high efficiency combustion with moderate peak pressure rise rates, low engine-out soot and NOx emissions. A key requirement for extending to high-load operation is reduce the reactivity of the premixed charge prior to the diesel injection. One way to accomplish this is to use a very low reactivity fuel such as natural gas. In this work, experimental testing was conducted on a 13L multi-cylinder heavy-duty diesel engine modified to operate using RCCI combustion with port injection of natural gas and direct injection of diesel fuel. Natural gas/diesel RCCI engine operation is compared over the EPA Heavy-Duty 13 mode supplemental emissions test with and without EGR.