Search Results

Fast charging is attractive to battery electric vehicle (BEV) drivers for its ability to enable long-distance travel and to quickly recharge depleted batteries on short notice. However, such aggressive charging and the sustained vehicle operation that results could lead to excessive battery temperatures and degradation. Properly assessing the consequences of fast charging requires accounting for disparate cycling, heating, and aging of individual cells in large BEV packs when subjected to realistic travel patterns, usage of fast chargers, and climates over long durations (i.e., years). The U.S. Department of Energy's Vehicle Technologies Office has supported the National Renewable Energy Laboratory's development of BLAST-V-the Battery Lifetime Analysis and Simulation Tool for Vehicles-to create a tool capable of accounting for all of these factors. We present on the findings of applying this tool to realistic fast charge scenarios.

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.

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.

The key hurdles to achieving wide consumer acceptance of battery electric vehicles (BEVs) are weather-dependent drive range, higher cost, and limited battery life. These translate into a strong need to reduce a significant energy drain and resulting drive range loss due to auxiliary electrical loads the predominant of which is the cabin thermal management load. Studies have shown that thermal sub-system loads can reduce the drive range by as much as 45% under ambient temperatures below −10 °C. Often, cabin heating relies purely on positive temperature coefficient (PTC) resistive heating, contributing to a significant range loss. Reducing this range loss may improve consumer acceptance of BEVs. The authors present a unified thermal management system (UTEMPRA) that satisfies diverse thermal and design needs of the auxiliary loads in BEVs.

Fuel consumption (FC) has always been an important factor in vehicle cost. With the advent of electronically controlled engines, the controller area network (CAN) broadcasts information about engine and vehicle performance, including fuel use. However, the accuracy of the FC estimates is uncertain. In this study, the researchers first compared CAN-broadcasted FC against physically measured fuel use for three different types of trucks, which revealed the inaccuracies of CAN-broadcast fueling estimates. To match precise gravimetric fuel-scale measurements, polynomial models were developed to correct the CAN-broadcasted FC. Lastly, the robustness testing of the correction models was performed. The training cycles in this section included a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. The mean relative differences were reduced noticeably.

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.

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.

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.

Both hydrogen and plug-in electric vehicles offer significant social and environmental benefits to enhance energy security and reduce criteria and greenhouse gas emissions from the transportation sector. However, the rollout of electric vehicle supply equipment (EVSE) and hydrogen retail stations (HRS) requires substantial investments with high risks due to many uncertainties. We compare retail infrastructure costs on a common basis - cost per mile, assuming fueling service to 10% of all light-duty vehicles in a typical 1.5 million person city in 2025. Our analysis considers three HRS sizes, four distinct types of EVSE and two distinct EVSE scenarios. EVSE station costs, including equipment and installation, are assumed to be 15% less than today's costs. We find that levelized retail capital costs per mile are essentially indistinguishable given the uncertainty and variability around input assumptions.

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).

The Kia Soul battery electric vehicle (BEV) is available with either a positive temperature coefficient (PTC) heater or an R134a heat pump (HP) with PTC heater combination [1]. The HP uses both ambient air and waste heat from the motor, inverter, and on-board-charger (OBC) for its heat source. Hanon Systems, Hyundai America Technical Center, Inc. (HATCI) and the National Renewable Energy Laboratory jointly, with financial support from the U.S. Department of Energy, developed and proved-out technologies that extend the driving range of a Kia Soul BEV while maintaining thermal comfort in cold climates. Improved system configuration concepts that use thermal storage and waste heat more effectively were developed and evaluated. Range extensions of 5%-22% at ambient temperatures ranging from 5 °C to −18 °C were demonstrated. This paper reviews the three-year effort, including test data of the baseline and modified vehicles, resulting range extension, and recommendations for future actions.

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.

The air-conditioning (A/C) compressor load significantly impacts the fuel economy of conventional vehicles and the fuel use/range of plug-in hybrid electric vehicles (PHEV). A National Renewable Energy Laboratory (NREL) vehicle performance analysis shows the operation of the air conditioner reduces the charge depletion range of a 40-mile range PHEV from 18% to 30% in a worst case hot environment. Designing for air conditioning electrical loads impacts PHEV and electric vehicle (EV) energy storage system size and cost. While automobile manufacturers have climate control procedures to assess A/C performance, and the U.S. EPA has the SCO3 drive cycle to measure indirect A/C emissions, there is no automotive industry consensus on a vehicle level A/C fuel use test procedure. With increasing attention on A/C fuel use due to increased regulatory activities and the development of PHEVs and EVs, a test procedure is needed to accurately assess the impact of climate control loads.

The projections of increased Extravehicular Activity (EVA) operations for the Space Station Freedom (SSF) resulted in the development of advanced space suit technologies to increase EVA efficiency. To eliminate the overhead of denitrogenation, candidate higher-operating pressure suit technologies were developed. The AX-5 all metallic, multi-bearing technologies were developed at the Ames Research Center, and the Mk. III fabric and metallic technologies were developed at the Johnson Space Center. Following initial technology development, extensive tests and analyses were performed to evaluate all aspects of candidate technology performance. The current Space Shuttle space suit technologies were used as a baseline for evaluating those of the AX-5 and Mk. III. Tests included manned evaluations in the Weightless Environment Training Facility and KC-135 zero-gravity aircraft.

Battery electric vehicles possess great potential for decreasing lifecycle costs in medium-duty applications, a market segment currently dominated by internal combustion technology. Characterized by frequent repetition of similar routes and daily return to a central depot, medium-duty vocations are well positioned to leverage the low operating costs of battery electric vehicles. Unfortunately, the range limitation of commercially available battery electric vehicles acts as a barrier to widespread adoption. This paper describes the National Renewable Energy Laboratory's collaboration with the U.S. Department of Energy and industry partners to analyze the use of small hydrogen fuel-cell stacks to extend the range of battery electric vehicles as a means of improving utility, and presumably, increasing market adoption.

A system performance study on a portable life support system being developed for use in the Weightless Environment Training Facility (WETF) and the Neutral Buoyancy Laboratory (NBL) has been completed. The Neutral Buoyancy Portable Life Support System (NBPLSS) will provide life support to suited astronauts training for extravehicular activity (EVA) under water without the use of umbilicals. The basic configuration is characterized by the use of medium pressure (200 - 300 psi) cryogen (liquid nitrogen/oxygen mixture) which provides cooling within the Extravehicular Mobility Unit (EMU), the momentum which enables flow in the vent loop, and oxygen for breathing. NBPLSS performance was analyzed by using a modified Metabolic Man program to compare competing configurations. Maximum sustainable steady state metabolic rates and transient performance based on a typical WETF metabolic rate profile were determined and compared.

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.

The National Renewable Energy Laboratory’s (NREL’s) CoolSim MATLAB/Simulink modeling framework was used to explore control strategies for an electric vehicle combined loop system. Three system variants of increased complexity and efficiency were explored: a glycol-based positive temperature coefficient heater (PTC), PTC with power electronics and electric motor (PEEM) waste heat recovery, and PTC with PEEM waste heat recovery plus heat pump versions. Additionally, the benefit of electric motor preheating was considered. A two-level control strategy was developed where the mode selection and component control were treated separately. Only the parameters typically available by vehicle sensors were used to control the system. The control approach included a mode selection algorithm and controllers for the compressor speed, cabin blower flow rate, coolant flow rate, and the front-end heat exchanger coolant bypass rate.