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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.

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.

National concerns over energy consumption and emissions from the transportation sector have prompted regulatory agencies to implement aggressive fuel economy targets for light-duty vehicles through the U.S. National Highway Traffic Safety Administration/Environmental Protection Agency (EPA) Corporate Average Fuel Economy (CAFE) program. Automotive manufacturers have responded by bringing competitive technologies to market that maximize efficiency while meeting or exceeding consumer performance and comfort expectations. In a collaborative effort among Toyota Motor Corporation, Argonne National Laboratory (ANL), and the National Renewable Energy Laboratory (NREL), the real-world savings of one such technology is evaluated. A commercially available Toyota Highlander equipped with two-phase cold storage technology was tested at ANL’s chassis dynamometer testing facility.

Battery second use-putting used plug-in electric vehicle (PEV) batteries into secondary service following their automotive tenure-has been proposed as a means to decrease the cost of PEVs while providing low cost energy storage to other fields (e.g., electric utility markets). To understand the value of used automotive batteries, however, we must first answer several key questions related to battery degradation, including: How long will PEV batteries last in automotive service? How healthy will PEV batteries be when they leave automotive service? How long will retired PEV batteries last in second-use service? How well can we best predict the second-use lifetime of a used automotive battery? Under the support of the U.S. Department of Energy's Vehicle Technologies Office, the National Renewable Energy Laboratory has developed a methodology and the requisite tools to answer these questions, including the Battery Lifetime Simulation Tool (BLAST).

In collaboration with the U.S. Environmental Protection Agency and the U.S. Department of Energy, the National Renewable Energy Laboratory has conducted a national analysis of road grade characteristics experienced by U.S. medium- and heavy-duty trucks on controlled access highways. These characteristics have been developed using TomTom’s commercially available street map and road grade database. Using the TomTom national road grade database, national statistics on road grade and hill distances were generated for the U.S. network of controlled access highways. These statistical distributions were then weighted using data provided by the U.S. Environmental Protection Agency for activity of medium- and heavy-duty trucks on controlled access highways. The national activity-weighted road grade and hill distance distributions were then used as targets for development of a handful of sample grade profiles potentially to be used in the U.S.

Vehicles continuously report real-time fuel consumption estimates over their data bus, known as the controller area network (CAN). However, the accuracy of these fueling estimates is uncertain to researchers who collect these data from any given vehicle. To assess the accuracy of these estimates, CAN-reported fuel consumption data are compared against fuel measurements from precise instrumentation. The data analyzed consisted of eight medium/heavy-duty vehicles and two medium-duty engines. Varying discrepancies between CAN fueling rates and the more accurate measurements emerged but without a vehicular trend-for some vehicles the CAN under-reported fuel consumption and for others the CAN over-reported fuel consumption. Furthermore, a qualitative real-time analysis revealed that the operating conditions under which these fueling discrepancies arose varied among vehicles.

This work details two approaches for evaluating transmission warming technology: experimental dynamometer testing and development of a simplified transmission efficiency model to quantify effects under varied real world ambient and driving conditions. Two vehicles were used for this investigation: a 2013 Ford Taurus and a highly instrumented 2011 Ford Fusion (Taurus and Fusion). The Taurus included a production transmission warming system and was tested over hot and cold ambient temperatures with the transmission warming system enabled and disabled. A robot driver was used to minimize driver variability and increase repeatability. Additionally the instrumented Fusion was tested cold and with the transmission pre-heated prior to completing the test cycles. These data were used to develop a simplified thermally responsive transmission model to estimate effects of transmission warming in real world conditions.

Today’s electric vehicle (EV) owners charge their vehicles mostly at home and seldom use public direct current fast charger (DCFCs), reducing the need for a large deployment of DCFCs for private EV owners. However, due to the emerging interest among transportation network companies to operate EVs in their fleet, there is great potential for DCFCs to be highly utilized and become economically feasible in the future. This paper describes a heuristic algorithm to emulate operation of EVs within a hypothetical transportation network company fleet using a large global positioning system data set from Columbus, Ohio. DCFC requirements supporting operation of EVs are estimated using the Electric Vehicle Infrastructure Projection tool. Operation and installation costs were estimated using real-world data to assess the economic feasibility of the recommended fast charging stations.

In a laboratory environment, it is cost prohibitive to run automotive battery aging experiments across a wide range of possible ambient environment, drive cycle, and charging scenarios. Because worst-case scenarios drive the conservative sizing of electric-drive vehicle batteries, it is useful to understand how and why those scenarios arise and what design or control actions might be taken to mitigate them. In an effort to explore this problem, this paper applies a semi-empirical life model of the graphite/nickel-cobalt-aluminum lithium-ion chemistry to investigate calendar degradation for various geographic environments and simplified cycling scenarios. The life model is then applied to analyze complex cycling conditions using battery charge/discharge profiles generated from simulations of plug-in electric hybrid vehicles (PHEV10 and PHEV40) vehicles across 782 single-day driving cycles taken from a Texas travel survey.

Hybrid electric vehicles, plug-in hybrid electric vehicles, and battery electric vehicles offer the potential to reduce both oil imports and greenhouse gases, as well as to offer a financial benefit to the driver. However, assessing these potential benefits is complicated by several factors, including the driving habits of the operator. We focus on driver aggression, i.e., the level of acceleration and velocity characteristic of travel, to (1) assess its variation within large, real-world drive datasets, (2) quantify its effect on both vehicle efficiency and economics for multiple vehicle types, (3) compare these results to those of standard drive cycles commonly used in the industry, and (4) create a representative drive cycle for future analyses where standard drive cycles are lacking.

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.

In 2011, the United States imported almost half of its petroleum. Lightweighting vehicles reduces that dependency directly by decreasing the engine, braking and rolling resistance losses, and indirectly by enabling a smaller, more efficiently operating engine to provide the same performance. The Future Automotive Systems Technology Simulator (FASTSim) tool was used to quantify these impacts. FASTSim is the U.S. Department of Energy's (DOE's) high-level vehicle powertrain model developed at the National Renewable Energy Laboratory. It steps through a time versus speed drive cycle to estimate the powertrain forces required to meet the cycle. It simulates the major vehicle powertrain components and their losses. It includes a cost model based on component sizing and fuel prices. FASTSim simulated different levels of lightweighting for four different powertrains.

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.

In this paper, researchers at the National Renewable Energy Laboratory present the results of simulation studies to evaluate potential fuel savings as a result of improvements to vehicle rolling resistance, coefficient of drag, and vehicle weight as well as hybridization for four powertrains for medium-duty parcel delivery vehicles. The vehicles will be modeled and simulated over 1,290 real-world driving trips to determine the fuel savings potential based on improvements to each technology and to identify best use cases for each platform. The results of impacts of new technologies on fuel saving will be presented, and the most favorable driving routes on which to adopt them will be explored.

This work presents a methodology to determine the off-cycle fuel economy benefit of a 2-Layer HVAC system which reduces ventilation and heat rejection losses of the heater core versus a vehicle using a standard system. Experimental dynamometer tests using EPA drive cycles over a broad range of ambient temperatures were conducted on a highly instrumented 2016 Lexus RX350 (3.5L, 8 speed automatic). These tests were conducted to measure differences in engine efficiency caused by changes in engine warmup due to the 2-Layer HVAC technology in use versus the technology being disabled (disabled equals fresh air-considered as the standard technology baseline). These experimental datasets were used to develop simplified response surface and lumped capacitance vehicle thermal models predictive of vehicle efficiency as a function of thermal state.

Commercial vehicle fuel economy is known to vary significantly with both positive and negative road grade. Medium- and heavy-duty vehicles operating at highway speeds require incrementally larger amounts of energy to pull heavy payloads up inclines as road grade increases. Non-hybrid vehicles are unable to recapture energy on descent and lose energy through friction braking. While the on-road effects of road grade are well understood, the majority of standard commercial vehicle drive cycles feature no road grade requirements. Additionally, the existing literature offers a limited number of sources that attempt to estimate the on-road energy implications of road grade in the medium- and heavy-duty space. This study uses real-world commercial vehicle drive cycles from the National Renewable Energy Laboratory's Fleet DNA database to simulate the effects of road grade on fuel economy across a range of vocations, operating conditions, and locations.

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.

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.

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.

Understanding the real-world power demand of modern automobiles is of critical importance to engineers using modeling and simulation in the design of increasingly efficient powertrains. Increased use of global positioning system (GPS) devices has made large-scale data collection of vehicle speed (and associated power demand) a reality. While the availability of real-world GPS data has improved the industry's understanding of in-use vehicle power demand, relatively little attention has been paid to the incremental power requirements imposed by road grade. This analysis quantifies the incremental efficiency impacts of real-world road grade by appending high-fidelity elevation profiles to GPS speed traces and performing a large simulation study. Employing a large, real-world dataset from the National Renewable Energy Laboratory's Transportation Secure Data Center, vehicle powertrain simulations are performed with and without road grade under five vehicle models.