Search Results

When operated, the cabin climate control system is the largest auxiliary load on a vehicle. This load has significant impact on fuel economy for conventional and hybrid vehicles, and it drastically reduces the driving range of all-electric vehicles (EVs). Heating is even more detrimental to EV range than cooling because no engine waste heat is available. Reducing the thermal loads on the vehicle climate control system will extend driving range and increase the market penetration of EVs. Researchers at the National Renewable Energy Laboratory have evaluated strategies for vehicle climate control load reduction with special attention toward grid-connected electric vehicles. Outdoor vehicle thermal testing and computational modeling were used to assess potential strategies for improved thermal management and to evaluate the effectiveness of thermal load reduction technologies. A human physiology model was also used to evaluate the impact on occupant thermal comfort.

The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL.

Accurate evaluation of vehicles' transient total power requirement helps achieving further improvements in vehicle fuel efficiency. When operated, the air-conditioning (A/C) system is the largest auxiliary load on a vehicle, therefore accurate evaluation of the load it places on the vehicle's engine and/or energy storage system is especially important. Vehicle simulation models, such as "Autonomie," have been used by OEMs to evaluate vehicles' energy performance. However, the load from the A/C system on the engine or on the energy storage system has not always been modeled in sufficient detail. A transient A/C simulation tool incorporated into vehicle simulation models would also provide a tool for developing more efficient A/C systems through a thorough consideration of the transient A/C system performance. The dynamic system simulation software MATLAB/Simulink® is frequently used by vehicle controls engineers to develop new and more efficient vehicle energy system controls.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

Plug-in hybrid electric vehicles (PHEVs) differ from hybrid vehicles (HEVs) with their ability to use off-board electricity generation to recharge their energy storage systems. In addition to possessing charge-sustaining HEV operation capability, PHEVs use the stored electrical energy during a charge-depleting operating period to displace a significant amount of petroleum consumption. The particular operating strategy employed during the charge-depleting mode will significantly influence the component attributes and the value of the PHEV technology. This paper summarizes three potential energy management strategies, and compares the implications of selecting one strategy over another in the context of the aggressiveness and distance of the duty cycle over which the vehicle will likely operate.

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).

Electrifying transportation can reduce or eliminate dependence on foreign fuels, emission of green house gases, and emission of pollutants. One challenge is finding a pathway for vehicles that gains wide market acceptance to achieve a meaningful benefit. This paper evaluates several approaches aimed at making plug-in electric vehicles (EV) and plug-in hybrid electric vehicles (PHEVs) cost-effective including opportunity charging, replacing the battery over the vehicle life, improving battery life, reducing battery cost, and providing electric power directly to the vehicle during a portion of its travel. Many combinations of PHEV electric range and battery power are included. For each case, the model accounts for battery cycle life and the national distribution of driving distances to size the battery optimally. Using the current estimates of battery life and cost, only the dynamically plugged-in pathway was cost-effective to the consumer.

Delphi and the National Renewable Energy Laboratory (NREL) collaborated to develop a simulation code to model the mechanical and electrical architectures of a series hybrid vehicle simultaneously. This co-simulation code is part of the larger ADVISOR® product created by NREL and diverse partners. Simulation of the macro power flow in a series hybrid vehicle requires both the mechanical drivetrain and the entire electrical architecture. It is desirable to solve the electrical network equations in an environment designed to comprehend such a network and solve the equations in terms of current and voltage. The electrical architecture for the series hybrid vehicle has been modeled in Saber™ to achieve these goals. This electrical architecture includes not only the high-voltage battery, generator, and traction motor, but also the normal low-voltage bus (14V) with loads common to all vehicles.

This paper describes the need for dynamic (transient) simulation of automotive air conditioning systems, the reasons why such simulations are challenging, and the applicability of a general purpose off-the-shelf thermohydraulic analyzer to answer such challenges. An overview of modeling methods for the basic components are presented, along with relevant approximations and their effect on speed and accuracy of the results.

One of the challenges of analyzing vehicular electrical systems is the co-dependence of the electrical system and the propulsion system. Even in traditional vehicles where the electrical power budget is very low, the electrical system analysis for macro power utilization over a drive cycle requires knowledge of the generator shaft rpm profile during the drive cycle. This co-dependence increases as the electrical power budget increases, and the integration of the two systems becomes complete when hybridization is chosen. Last year at this conference, the authors presented a paper entitled “Dual Voltage Electrical System Simulations.” That paper established validation for a suite of electrical component models and demonstrated the ability to predict system performance both on a macro power flow (entire drive cycle) level and a detailed transient-event level. The techniques were applicable to 12V, 42V, dual voltage, and/or elevated voltage systems.

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.

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.

How much fuel does vehicle air conditioning actually use? This study attempts to answer that question to determine the national and state-by-state fuel use impact seen by using air conditioning in light duty gasoline vehicles. The study used data from US cities, representative of averages over the past 30 years, whose temperature, incident radiation, and humidity varied through time of day and day of year. National surveys estimated when people drive their vehicles during the day and throughout the year. A simple thermal comfort model based on Fanger's heat balance equations determined the percentage of time that a driver would use the air conditioning based on the premise that if a person were dissatisfied with the thermal environment, they would turn on the air conditioning. Vehicle simulations for typical US cars and trucks determined the fuel economy reduction seen with AC use.

Global Positioning System (GPS) data acquisition devices have proven useful tools for gathering real-world driving data and statistics. The data collected by these devices provide valuable information in studying driving habits and conditions. When used jointly with vehicle simulation software, the data are invaluable in analyzing vehicle fuel use and performance, aiding in the design of more advanced and efficient vehicle technologies. However, when employing GPS data acquisition systems to capture vehicle drive-cycle information, a number of errors often appear in the captured raw data samples. Common sources of error in GPS data include sudden signal loss, extraneous or outlying data points, speed drifting, and signal white noise, all of which combine to limit the quality of field data for use in downstream applications.

A simple cost analysis framework compares hydrogen and electricity as energy carriers delivering wind energy to light-duty vehicles (LDVs). We compare four wind energy pathways within a 2040-2050 timeframe and at large scale: a dedicated electricity transmission pathway and three distinct wind-hydrogen delivery pathways. Our results suggest that wind-hydrogen pathways will tend to be more costly than pure electricity transmission pathways on a per-mile driven cost basis ($/mile), but to a greater or lesser degree depending upon the pathway. The additional cost could be warranted to the degree that the hydrogen pathway adds value to consumers through full performance fuel cell electric vehicles (FCEV) compared to plug-in electric vehicles (PEVs), or through reduced variability in wind energy supply. If these benefits add value beyond the incremental costs suggested by our simple cost framework, some shift toward co-production or even dedicated hydrogen wind farms may be warranted.

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.

Hybrid diesel electric buses offer the advantage of superior fuel economy through use of regenerative braking and lowered transient emissions by reducing the need of the engine to follow load as closely as in a conventional bus. With the support of the Department of Energy (DOE), five Lockheed Martin-Orion hybrid diesel-electric buses were operated on the West Virginia University Transportable Laboratory in Brooklyn, New York. The buses were exercised through a new cycle, termed the Manhattan cycle, that was representative of today's bus use as well as the accepted Central Business District Cycle and New York Bus Cycle. Emissions data were corrected for the state of charge of the batteries. The emissions can be expressed in units of grams/mile, grams/axle hp-hr and grams/gallon fuel. The role of improved fuel economy in reducing oxides of nitrogen relative to conventional automatic buses is evident in the data.

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.

As the size of spacecraft decreases, the contribution of the power subsystem to the overall spacecraft weight significantly increases. This paper will focus on describing a prototype solar array utilizing three promising technologies to significantly reduce weight, deploy with low shock, and increase packaging efficiency of the solar power system. These technologies are: Copper Indium DiSelenide (CIS) Thin-Film Photovoltaics, Smart Mechanisms Employing Shape Memory, and Multifunctional Structures. Recent advances in shape memory alloy devices, ultralight composites, along with thin-film copper indium diselenide (CIS or CuInSe2) photovoltaics (PV), have shown the potential of providing solar array systems with overall array specific power of >100 W/kg. This results in solar arrays that are a factor of 5 lighter than the current state-of-the-practice, and a factor of 3 lighter than the state-of-the-art.

Concern over the possibility of global climate change as a result of anthropogenic greenhouse gas buildup in the atmosphere is resulting in increased interest in renewable energy technologies. The World Bank recently sponsored a study to determine whether solar thermal power plants can achieve cost parity with conventional power plants. The paper reviews the conclusions of that study.