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As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory’s Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation.

Increased market penetration of electric drive vehicles (EDVs) requires overcoming a number of hurdles, including limited vehicle range and the elevated cost in comparison to conventional vehicles. Climate control loads have a significant impact on range, cutting it by over 50% in both cooling and heating conditions. To minimize the impact of climate control on EDV range, the National Renewable Energy Laboratory has partnered with Hyundai America and key industry partners to quantify the performance of thermal load reduction technologies on a Hyundai Sonata plug-in hybrid electric vehicle. Technologies that impact vehicle cabin heating in cold weather conditions and cabin cooling in warm weather conditions were evaluated. Tests included thermal transient and steady-state periods for all technologies, including the development of a new test methodology to evaluate the performance of occupant thermal conditioning.

The National Renewable Energy Laboratory (NREL) has conducted a series of chassis dynamometer and road tests on the 2000 model-year Honda Insight. This paper will focus on results from the testing, how the results have been applied to NREL's Advanced Vehicle Simulator (ADVISOR), and how test results compare to the model predictions and published data. The chassis dynamometer testing included the FTP-75 emissions certification test procedure, highway fuel economy test, US06 aggressive driving cycle conducted at 0°C, 20°C, and 40°C, and the SC03 test performed at 35°C with the air conditioning on and with the air conditioning off. Data collection included bag and continuously sampled emissions (for the chassis tests), engine and vehicle operating parameters, battery cell temperatures and voltages, motor and auxiliary currents, and cabin temperatures.

Annual fuel use for long-haul truck rest period idling is estimated at 667 million gallons in the United States. The U.S. Department of Energy's 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 climate control systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In order for candidate idle reduction technologies to be implemented at the original equipment manufacturer and fleet level, their effectiveness must be quantified. To address this need, a number of promising candidate technologies were evaluated through experimentation and modeling to determine their effectiveness in reducing rest period HVAC loads.

It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts.

A new type of solar-reflective glass that improves reflection of the near-infrared (NIR) portion of the solar spectrum has been developed. Also developed was a prototype solar-reflective paint that increases the NIR reflection of opaque vehicle surfaces while maintaining desired colors in the visible portion of the spectrum. Both of these technologies, as well as solar-powered parked car ventilation, were tested on a Cadillac STS as part of the Improved Mobile Air Conditioning Cooperative Research Program (I-MAC). Significant reductions in interior and vehicle skin temperatures were measured. The National Renewable Energy Laboratory (NREL) performed an analysis to determine the impact of reducing the thermal load on the vehicle. A simplified cabin thermal/fluid model was run to predict the potential reduction in A/C system capacity. The potential reduction in fuel use was calculated using a vehicle simulation tool developed by the U.S. Department of Energy (DOE).

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver.

The National Renewable Energy Laboratory's (NREL's) Fleet Test and Evaluations team recently conducted chassis dynamometer tests of a class 8 conventional regional delivery truck over the Heavy Heavy-Duty Diesel Truck (HHDDT), West Virginia University City (WVU City), and Composite International Truck Local and Commuter Cycle (CILCC) drive cycles. A quantitative study analyzed the impacts of various factors on fuel consumption (FC) and fuel economy (FE) by modeling and simulating the truck using NREL's Future Automotive Systems Technology Simulator (FASTSim). Factors included vehicle weight and the coefficients of rolling resistance and aerodynamic drag. Simulation results from a single parametric study revealed that FC was approximately a linear function of the weight, coefficient of aerodynamic drag, and rolling resistance over various drive cycles.

The disparate characteristics between conventional (CVs) and battery electric vehicles (BEVs) in terms of driving range, refill/recharge time, and availability of refuel/recharge infrastructure inherently limit the relative utility of BEVs when benchmarked against traditional driver travel patterns. However, given a high penetration of high-power public charging combined with driver tolerance for rerouting travel to facilitate charging on long-distance trips, the difference in utility between CVs and BEVs could be marginalized. We quantify the relationships between BEV utility, the deployment of fast chargers, and driver tolerance for rerouting travel and extending travel durations by simulating BEVs operated over real-world travel patterns using the National Renewable Energy Laboratory's Battery Lifetime Analysis and Simulation Tool for Vehicles (BLAST-V). With support from the U.S.

With a growing need for a more efficient consumer based automotive platform, Embry-Riddle Aeronautical University (ERAU) chose to redesign the 2013 Chevrolet Malibu as a Plug-in Hybrid Electric Vehicle(PHEV). A Series architecture was chosen for its low energy consumption and high consumer acceptability when compared to the Series/Parallel-through-the-road and the Pre-Transmission designs. A fuel selection process was also completed and B20 Biodiesel was selected as the primary fuel due to lower GHG (Greenhouse Gases) emissions and Embry-Riddle's ability to produce biodiesel onsite using the cafeteria's discarded vegetable oil.

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.

Electric vehicles (EVs) need highly optimized thermal management systems to improve range. Climate control can reduce vehicle efficiency and range by more than 50%. Due to the relative shortage of waste heat, heating the passenger cabin in EVs is difficult. Cabin cooling can take a high portion of the energy available in the battery. Compared to internal combustion engine-driven vehicles, different heating methods and more efficient cooling methods are needed, which can make EV thermal management systems more complex. More complex systems typically allow various alternative modes of operation that can be selected based on driving and ambient conditions. A good system simulation tool can greatly reduce the time and expense for developing these complex systems. A simulation model should also be able to efficiently co-simulate with vehicle simulation programs, and should be applicable for evaluating various control algorithms.

This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other three as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method.

Transportation vehicle and network system efficiency can be defined in two ways: 1) reduction of travel times across all the vehicles in the system, and 2) reduction in total energy consumed by all the vehicles in the system. The mechanisms to realize these efficiencies are treated as independent (i.e., vehicle and network domains) and, when combined, they have not been adequately studied to date. This research aims to integrate previously developed and published research on Predictive Optimal Energy Management Strategies (POEMS) and Intelligent Traffic Systems (ITS), to address the need for quantifying improvement in system efficiency resulting from simultaneous vehicle and network optimization. POEMS and ITS are partially independent methods which do not require each other to function but whose individual effectiveness may be affected by the presence of the other. In order to evaluate the system level efficiency improvements, the Mobility Energy Productivity (MEP) metric is used.

With support from the U.S. Department of Energy's Vehicle Technologies Office, the National Renewable Energy Laboratory developed BLAST-V-the Battery Lifetime Analysis and Simulation Tool for Vehicles. The addition of high-resolution spatial-temporal travel histories enables BLAST-V to investigate user-defined infrastructure rollouts of publically accessible charging infrastructure, as well as quantify impacts on vehicle and station owners in terms of improved vehicle utility and station throughput. This paper presents simulation outputs from BLAST-V that quantify the utility improvements of multiple distinct rollouts of publically available Level 2 electric vehicle supply equipment (EVSE) in the Seattle, Washington, metropolitan area. Publically available data on existing Level 2 EVSE are also used as an input to BLAST-V. The resulting vehicle utility is compared to a number of mock rollout scenarios.

The air-conditioning (A/C) system compressor load can significantly impact the fuel economy and tailpipe emissions of conventional and hybrid electric automobiles. With the increasing emphasis on fuel economy, it is clear that the A/C compressor load needs to be reduced. In order to accomplish this goal, more efficient climate control delivery systems and reduced peak soak temperatures will be necessary to reduce the impact of vehicle A/C systems on fuel economy and tailpipe emissions. Good analytical techniques are important in identifying promising concepts. The goal at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is to assess thermal comfort, fuel economy, and emissions by using an integrated modeling approach composed of CAD, computational fluid dynamics (CFD), thermal comfort, and vehicle simulation tools. This paper presents NREL's vehicle integrated modeling process.

The objective of the study was to assess the impact of a Saflex1 S Series solar control PVB (polyvinyl butyral) windshield on conventional vehicle fuel economy and electric vehicle (EV) range. The approach included outdoor vehicle thermal soak testing, RadTherm cooldown analysis, and vehicle simulations. Thermal soak tests were conducted at the National Renewable Energy Laboratory's Vehicle Testing and Integration Facility in Golden, Colorado. The test results quantified interior temperature reductions and were used to generate initial conditions for the RadTherm cooldown analysis. The RadTherm model determined the potential reduction in air-conditioning (A/C) capacity, which was used to calculate the A/C load for the vehicle simulations. The vehicle simulation tool identified the potential reduction in fuel consumption or improvement in EV range between a baseline and solar control PVB configurations for the city and highway drive cycles.

Cab climate conditioning is one of the primary reasons for operating the main engine in a long-haul truck during driver rest periods. In the United States, sleeper cab trucks use approximately 667 million gallons of fuel annually for rest period idling. The U.S. Department of Energy's National Renewable Energy Laboratory's (NREL) CoolCab Project works closely with industry to design efficient thermal management systems for long-haul trucks that minimize engine idling and fuel use while maintaining occupant comfort. Heat transfer to the vehicle interior from opaque exterior surfaces is one of the major heat pathways that contribute to air conditioning loads during long-haul truck daytime rest period idling. To quantify the impact of paint color and the opportunity for advanced paints, NREL collaborated with Volvo Group North America, PPG Industries, and Dometic Environmental Corporation.

A turbocharged lean burn natural gas engine was upgraded to operate on a blend of hydrogen and natural gas (HCNG). Tests were carried out to determine the most suitable H2/NG blend for H2 fractions between 20 and 32 vol%. A 20 vol% H2 content was found to provide the desired benefits when taking into consideration the engine and vehicle performance attributes. A full engine map was developed for the chosen mixture, and was verified over the steady-state AVL8 cycle. In general, the HCNG calibration included operation at higher air-fuel ratios and retarded spark timings. The results indicated that the NOx and NMHC emissions were reduced by 50% and 58% respectively, while the CO and CH4 emissions were slightly reduced. The HCNG engine torque, power and fuel consumption were maintained the same as for the natural gas fuel. The chassis dynamometer transient testing confirmed large NOx reduction of about 56% for HCNG operation.