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With the increasing demand for Battery Electric Vehicles (BEVs) capable of extended mileage, optimizing their efficiency has become paramount for manufacturers. However, the challenge lies in balancing the need for climate control within the cabin and precise thermal regulation of the battery, which can significantly reduce a vehicle's driving range, often leading to energy consumption exceeding 50% under severe weather conditions. To address these critical concerns, this study embarks on a comprehensive exploration of the impact of weather conditions on energy consumption and range for the 2019 Nissan Leaf Plus. The primary objective of this research is to enhance the understanding of thermal management for BEVs by introducing a sophisticated thermal management system model, along with detailed thermal models for both the battery and the cabin.

In response to the stipulations of the Energy Policy and Conservation Act and the global momentum toward carbon mitigation, there has been a pronounced tightening of fuel economy standards for manufacturers. This stricter regulation is coupled with an accelerated transition to electric vehicles, catalyzed by advances in electrification technology and a decline in battery cost. Improvements in the fuel economy of medium- and heavy-duty vehicles through electrification are particularly noteworthy. Estimating the magnitude of fuel economy improvements that result from technological advances in these vehicles is key to effective policymaking. In this research, we generated vehicle models based on assumptions regarding advanced transportation component technologies and powertrains to estimate potential vehicle-level fuel savings. We also developed a systematic approach to evaluating a vehicle’s fuel economy by calibrating the size of the components to satisfy performance requirements.

The National Highway Traffic Safety Administration (NHTSA) has been leading U.S. efforts related to the rulemaking process for Corporate Average Fuel Economy (CAFE) standards. Argonne National Laboratory, a U.S. Department of Energy (DOE) national laboratory, has developed a full-vehicle simulation tool called Autonomie that has become one of the industry standard tools for analyzing vehicle performance, energy consumption, and technology effectiveness. Through an Interagency Agreement, the DOE Argonne Site Office and Argonne National Laboratory have been tasked with conducting full vehicle simulation to support NHTSA CAFE rulemaking. This paper presents an innovative approach focused on large-scale simulation processes spanning standard regulatory driving cycles, diverse vehicle classes, and various timeframes. A key element of this approach is Autonomie’s capacity to integrate advanced engine technologies tailored to specific vehicle classes and powertrains.

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

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.

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.

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.

It is widely understood that cold ambient temperatures negatively impact vehicle system efficiency. This is due to a combination of factors: increased friction (engine oil, transmission, and driveline viscous effects), cold start enrichment, heat transfer, and air density variations. Although the science of quantifying steady-state vehicle component efficiency is mature, transient component efficiencies over dynamic ambient real-world conditions is less understood and quantified. This work characterizes wheel assembly efficiencies of a conventional and electric vehicle over a wide range of ambient conditions. For this work, the wheel assembly is defined as the tire side axle spline, spline housing, bearings, brakes, and tires. Dynamometer testing over hot and cold ambient temperatures was conducted with a conventional and electric vehicle instrumented to determine the output energy losses of the wheel assembly in proportion to the input energy of the half-shafts.

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 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).

The Rotating Liner Engine (RLE) is an engine design concept where the cylinder liner rotates in order to reduce piston assembly friction and liner/ring wear. The reduction is achieved by the elimination of the mixed and boundary lubrication regimes that occur near TDC. Prior engines for aircraft developed during WW2 with partly rotating liners (Sleeve Valve Engines or SVE) have exhibited reduction of bore wear by factor of 10 for high BMEP operation, which supports the elimination of mixed lubrication near the TDC area via liner rotation. Our prior research on rotating liner engines experimentally proved that the boundary/mixed components near TDC are indeed eliminated, and a high friction reduction was quantified compared to a baseline engine. The added friction required to rotate the liner is hydrodynamic via a modest sliding speed, and is thus much smaller than the mixed and boundary friction that is eliminated.

EmiSense Technologies, LLC (www.emisense.com) is commercializing its electronic particulate matter (PM) sensor that is based on technology developed at the University of Texas at Austin (UT). To demonstrate the capability of this sensor for real-time PM measurements and on board diagnostics (OBD) for failure detection of diesel particle filters (DPF), independent measurements were performed to characterize the engine PM emissions and to compare with the PM sensor response. Computational fluid dynamics (CFD) modeling was performed to characterize the hydrodynamics of the sensor's housing and to develop an improved PM sensor housing with reproducible hydrodynamics and an internal baffle to minimize orientation effects. PM sensors with the improved housing were evaluated in the truck exhaust of a heavy duty (HD) diesel engine tested on-road and on a chassis dynamometer at the University of California, Riverside (UCR) using their Mobile Emissions Laboratory (MEL).

Argonne National Laboratory (Argonne) and Idaho National Laboratory (INL), working with the FreedomCAR and Fuels Partnership, lead activities in vehicle dynamometer and fleet testing as well as in modeling activities. By using Argonne’s Advanced Powertrain Research Facility (APRF), the General Motors (GM) Tahoe 2-mode was instrumented and tested in the 4-wheel-drive test facility. Measurements included both sensors and controller area network (CAN) messages. In this paper, we describe the vehicle instrumentation as well as the test results. On the basis of the analysis performed, we discuss the vehicle model developed in Argonne’s vehicle simulation tool, the Powertrain System Analysis Toolkit (PSAT), and its comparison with test data. Finally, on-road vehicle data, performed by INL, is discussed and compared with the dynamometer results.

As natural gas engines are designed to operate leaner and with increased boost pressure, durability of the spark plugs becomes problematic. Among the various new ignition devices that have been considered to solve some of the problems facing spark plugs, railplugs appear to hold clear advantages in some areas. There are two types of railplugs: coaxial rail and parallel rail. This paper reports the results of an experimental study of various parameters that affect the performance of parallel railplugs. Their performance was quantified by the distance that the arc traveled along the rails from the initiation point. Travel along the rails is thought to be an important performance metric because rail-travel limits excessive local wear and produces a distributed ignition source which can potentially reduce mixture inhomogeneity induced ignition problems.

It is a very challenging problem to reliably ignite extremely lean mixtures, especially for the low speed, high load conditions of large-bore natural gas engines. If these engines are to be use for the distributed power generation market, it will require operation with higher boost pressures and even leaner mixtures. Both place greater demands on the ignition system. The railplug is a very promising ignition system for lean burn natural gas engines with its high-energy deposition and high velocity plasma arc. It requires care to properly design railplugs for this new application, however. For these engines, in-cylinder pressure and mixture temperature are very high at the time of ignition due to the high boost pressure. Hot spots may exist on the electrodes of the ignitor, causing pre-ignition problems. A heat transfer model is proposed in this paper to aid the railplug design. The electrode temperature was measured in an operating natural gas engine.

The On-Board Distillation System (OBDS) extracts, from gasoline, a highly volatile crank fuel that enables simultaneous reduction of start-up fuel enrichment and significant ignition timing retard during cold-starting. In a previous paper we reported reductions in catalyst light-off time of >50% and THC emissions reductions >50% over Phase I of the FTP drive cycle. The research presented herein is a further development of the OBDS concept. For this work, OBDS was improved to yield higher-quality start-up fuel. The PCM calibration was changed as well, in order to improve the response to intake manifold pressure transients. The test vehicle was tested over the 3-phase FTP, with exhaust gases speciated to determine NMOG and exhaust toxics emissions. Also, the effectiveness of OBDS at generating a suitable starting fuel from a high driveability index test gasoline was evaluated.

Time-resolved current and voltage measurements for an inductive automotive spark system were made. Also presented are measurements of the total energy delivered to the spark gap. The measurements were made in air for a range of pressures from 1-18 atm, at ambient temperatures. The measured voltage and current characteristics were found to be a function of many ignition parameters; some of these include: spark gap distance, internal resistance of the spark plug and high tension wire, and pressure. The voltages presented were measured either at the top of the spark plug or at the spark gap. The measurements were made at different time resolutions to more accurately resolve the voltage and current behavior throughout the discharge process. This was necessary because the breakdown event occurs on a time scale much shorter than the arc and glow phases.

Piston wetting can be isolated from the other sources of HC emissions from DISI engines by operating the engine predominantly on a gaseous fuel and using an injector probe to impact a small amount of liquid fuel on the piston top. This results in a marked increase in HC emissions. In a previous study, we used a variety of pure liquid hydrocarbon fuels to examine the influence of fuel volatility and structure on the HC emissions due to piston wetting. It was shown that the HC emissions correspond to the Leidenfrost effect: fuels with very low boiling points yield high HCs and those with a boiling point near or above the piston temperature produce much lower HCs. All of these prior tests of fuel effects were performed at a single operating condition: the Ford World Wide Mapping Point (WWMP). In the present study, the effects of load and engine speed are examined.

Two limited-production hybrid electric vehicles (HEVs) - a 1988 Japanese model Toyota Prius and a 2000 Honda Insight - were tested at Argonne National Laboratory to collect data from vehicle component and systems operation. The test data are used to analyze operation and efficiency and to help validate computer simulation models. Both HEVs have FTP fuel economy greater than 45 miles per gallon and also have attributes very similar to those of conventional gasoline vehicles, even though each HEV has a unique powertrain configuration and operation control strategy. The designs and characteristics of these vehicles are of interest because they represent production technology with all the compromises for production included. This paper will explore both designs, their control strategies, and under what conditions high fuel economy was achieved.

The effects of fuel properties on the emissions of a production vehicle with a gasoline direct injection engine operating over the Federal Test Procedure (FTP) cycle were investigated. The vehicle used was a 1998 Toyota Corona passenger car with a direct injection spark ignition (DISI) engine. Engine-out and tailpipe FTP emissions for six fuels and a California Phase 2 RFG reference fuel are presented. Four of the test fuels were blended from refinery components to meet specified distillation profiles. The remaining test fuels were iso-octane and toluene, an iso-alkane and an aromatic with essentially the same boiling point (at atmospheric pressure) that is near the T50 point for the blended fuels. Statistically significant effects, at the 95% confidence level, of the fuels on tailpipe emissions were found. Correlations were sought between the properties of the five blends and the Emissions Indices for engine-out hydrocarbons and NOx and for tailpipe particulates.