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Development of 80- and 100- Mile Work Day Cycles Representative of Commercial Pickup and Delivery Operation
4/3/2018
When developing and designing new technology for integrated vehicle systems deployment, standard cycles have long existed for chassis dynamometer testing and tuning of the powertrain. However, to this day with recent developments and advancements in plug-in hybrid and battery electric vehicle technology, no true 'work day' cycles exist with which to tune and measure energy storage control and thermal management systems. To address these issues and in support of development of a range-extended pickup and delivery Class 6 commercial vehicle, researchers at the National Renewable Energy Laboratory in collaboration with Cummins analyzed 78,000 days of operational data captured from more than 260 vehicles operating across the United States to characterize the typical daily performance requirements associated with Class 6 commercial pickup and delivery operation. In total, over 2.5 million miles of real-world vehicle operation were condensed into a pair of duty cycles, an 80-mile cycle and a 100-mile cycle representative of the daily operation of U.S. class 3-6 commercial pickup and delivery trucks. Using novel machine learning clustering methods combined with mileage-based weighting, these composite representative cycles correspond to 90th and 95th percentiles for daily vehicle miles traveled by the vehicles observed. In addition to including vehicle speed vs time drive cycles, in an effort to better represent the environmental factors encountered by pickup and delivery vehicles operating across the United States, a nationally representative grade profile and key status information were also appended to the speed vs. time profiles to produce a 'work day' cycle that captures the effects of vehicle dynamics, geography, and driver behavior which can be used for future design, development, and validation of technology.
Authors: Duran, A.; Li, K.; Kresse, J.; Kelly, K.
Range Extension Opportunities While Heating a Battery Electric Vehicle. SAE Paper No. 2018-01-0066
4/3/2018
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 degrees C to -18 degrees 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.
Authors: Meyer, J.J.; Lustbader, J.; Agathocleous, N.; Vespa, A.; Rugh, J.; Titov, G.
Influences on Energy Savings of Heavy Trucks Using Cooperative Adaptive Cruise Control
4/3/2018
An integrated adaptive cruise control (ACC) and cooperative ACC (CACC) was implemented and tested on three heavy-duty tractor-trailer trucks on a closed test track. The first truck was always in ACC mode, and the followers were in CACC mode using wireless vehicle-vehicle communication to augment their radar sensor data to enable safe and accurate vehicle following at short gaps. The fuel consumption for each truck in the CACC string was measured using the SAE J1321 procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb, demonstrating the effects of: inter-vehicle gaps (ranging from 3.0 s or 87 m to 0.14 s or 4 m, covering a much wider range than previously reported tests), cut-in and cut-out maneuvers by other vehicles, speed variations, the use of mismatched vehicles (standard trailers mixed with aerodynamic trailers with boat tails and side skirts), and the presence of a passenger vehicle ahead of the platoon. The results showed that energy savings generally increased in a non-linear fashion as the gap was reduced. The middle truck saved the most fuel at gaps shorter than 12 m and the trailing truck saved the most at longer gaps, while lead truck saved the least at all gaps. The cut-in and cut-out maneuvers had only a marginal effect on fuel consumption even when repeated every two miles. The presence of passenger-vehicle traffic had a measurable impact. The fuel-consumption savings on the curves was less than on the straight sections.
Authors: McAuliffe, B.; Lamert, M.; Lu, X.-Y.; Shladover, S.; Surcel, M.-D.; Kailas, A.
Clean Cities Alternative Fuel Price Report, January 2018
3/29/2018
The Clean Cities Alternative Fuel Price Report for January 2018 is a quarterly report on the prices of alternative fuels in the U.S. and their relation to gasoline and diesel prices. This issue describes prices that were gathered from Clean Cities coordinators and stakeholders between January 1, 2018 and January 16, 2018, and then averaged in order to determine regional price trends by fuel and variability in fuel price within regions and among regions. The prices collected for this report represent retail, at-the-pump sales prices for each fuel, including Federal and state motor fuel taxes.
Table 2 reports that the nationwide average price (all amounts are per gallon) for regular gasoline has increased 1 cent from $2.49 to $2.50; diesel increased 20 cents from $2.76 to $2.96; CNG remained the same at $2.17; ethanol (E85) decreased 4 cents from $2.10 to $2.06; propane increased 5 cents from $2.78 to $2.83; and biodiesel (B20) increased 16 cents from 2.68 to $2.84.
According to Table 3, CNG is $.33 less than gasoline on an energy-equivalent basis, while E85 is $0.18 more than gasoline on an energy-equivalent basis.
Authors: Bourbon, E.
California Plug-In Electric Vehicle Infrastructure Projections: 2017-2025 - Future Infrastructure Needs for Reaching the State's Zero Emission-Vehicle Deployment Goals
3/27/2018
This report analyzes plug-in electric vehicle (PEV) infrastructure needs in California from 2017 to 2025 in a scenario where the State's zero-emission vehicle (ZEV) deployment goals are achieved by household vehicles. The statewide infrastructure needs are evaluated by using the Electric Vehicle Infrastructure Projection tool, which incorporates representative statewide travel data from the 2012 California Household Travel Survey. The infrastructure solution presented in this assessment addresses two primary objectives: (1) enabling travel for battery electric vehicles and (2) maximizing the electric vehicle-miles traveled for plug-in hybrid electric vehicles. The analysis is performed at the county-level for each year between 2017 and 2025 while considering potential technology improvements. The results from this study present an infrastructure solution that can facilitate market growth for PEVs to reach the State's ZEV goals by 2025. The overall results show a need for 99k-130k destination chargers, including workplaces and public locations, and 9k-25k fast chargers. The results also show a need for dedicated or shared residential charging solutions at multi-family dwellings, which are expected to host about 120k PEVs by 2025. An improvement to the scientific literature, this analysis presents the significance of infrastructure reliability and accessibility on the quantification of charger demand.
Authors: Bedir, A.; Crisostomo, N.; Allen, J.; Wood, E.; Rames, C.
Estimating Highway Volumes Using Vehicle Probe Data - Proof of Concept: Preprint
3/12/2018
This paper examines the feasibility of using sampled commercial probe data in combination with validated continuous counter data to accurately estimate vehicle volume across the entire roadway network, for any hour during the year. Currently either real time or archived volume data for roadways at specific times are extremely sparse. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper explores the use of commercial probe data to generate accurate volume measures that span the highway network providing ubiquitous coverage in space, and specific point-in-time measures for a specific date and time. The paper examines the need for the data, fundamental accuracy limitations based on a basic statistical model that take into account the sampling nature of probe data, and early results from a proof of concept exercise revealing the potential of probe type data calibrated with public continuous count data to meet end user expectations in terms of accuracy of volume estimates.
Authors: Young, S.E.; Hou, Y.; Sadabadi, K.; Sekula, P.; Markow, D.
Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green Routing Applications
2/12/2018
A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages nearly 1 million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rates look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to almost any type of driving data. The model has been trained on vehicle global positioning system data from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. The estimation model has demonstrated an error range of 8.6% to 13.8%. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.
Authors: Holden, J.; Van Til, H.; Wood, E.; Zhu, L.; Gonder, J.; Shirk, M.
Initial Assessment and Modeling Framework Development for Automated Mobility Districts: Preprint
2/7/2018
Automated vehicles (AVs) are increasingly being discussed as the basis for on-demand mobility services, introducing a new paradigm in which a fleet of AVs displaces private automobiles for day-to-day travel in dense activity districts. This paper examines a concept to displace privately owned automobiles within a region containing dense activity generators (jobs, retail, entertainment, etc.), referred to as an automated mobility district (AMD). This paper reviews several such districts, including airports, college campuses, business parks, downtown urban cores, and military bases, with examples of previous attempts to meet the mobility needs apart from private automobiles, some with automated technology and others with more traditional transit-based solutions. The issues and benefits of AMDs are framed within the perspective of intra-district, inter-district, and border issues, and the requirements for a modeling framework are identified to adequately reflect the breadth of mobility, energy, and emissions impact anticipated with AMDs.
Authors: Young, S.E.; Hou, Y.; Garikapati, V.; Chen, Y.; Zhu, L.
Charging Electric Vehicles in Smart Cities: An EVI-Pro Analysis of Columbus, Ohio
2/7/2018
With the support of the U.S. Department of Energy's Vehicle Technologies Office, the National Renewable Energy Laboratory (NREL) worked with the City of Columbus, Ohio, to develop a plan for the expansion of the region's network of charging stations to support increased adoption of plug-in electric vehicles (PEVs) in the local market. NREL's Electric Vehicle Infrastructure Projection (EVI-Pro) model was used to generate scenarios of regional charging infrastructure to support consumer PEV adoption. Results indicate that approximately 400 Level 2 plugs at multi-unit dwellings and 350 Level 2 plugs at non-residential locations are required to support Columbus' primary PEV goal of 5,300 PEVs on the road by the end of 2019. This analysis finds that while consumer demand for fast charging is expected to remain low (due to modest anticipated adoption of short-range battery electric vehicles), a minimum level of fast charging coverage across the city is required to ease consumer range anxiety concerns by providing a safety net for unexpected charging events. Sensitivity analyses around some key assumptions have also been performed; of these, consumer preference for PHEV versus BEV and for their electric driving range, ambient conditions, and availability of residential charging at multi-unit dwellings were identified as key determinants of the non-residential PEV charging infrastructure required to support PEV adoption. The results discussed in this report can be leveraged by similar U.S. cities as part of a strategy to accelerate PEV adoption in the light-duty vehicle market.
Authors: Wood, E.; Rames, C.; Muratori, M.; Raghavan, S.; Young, S.
Correlations of Platooning Track Test and Wind Tunnel Data
2/5/2018
In this report, the National Renewable Energy Laboratory analyzed results from multiple, independent truck platooning projects to compare and contrast track test results with wind tunnel test results conducted by Lawrence Livermore National Laboratory (LLNL). Some highlights from the report include compiled data, and results from four independent SAE J1321 full-size track test campaigns that were compared to LLNL wind tunnel testing results. All platooning scenarios tested demonstrated significant fuel savings with good correlation relative to following distances, but there are still unanswered questions and clear opportunities for system optimization. NOx emissions showed improvements from NREL tests in 2014 to Auburn tests in 2015 with respect to J1321 platooning track testing of Peloton system. NREL evaluated data from Volpe's Naturalistic Study of Truck Following Behavior, which showed minimal impact of naturalistic background platooning. We found significant correlation between multiple track studies, wind tunnel tests, and computational fluid dynamics, but also showed that there is more to learn regarding close formation and longer-distance effects. We also identified potential areas for further research and development, including development of advanced aerodynamic designs optimized for platooning, measurement of platoon system performance in traffic conditions, impact of vehicle lateral offsets on platooning performance, and characterization of the national potential for platooning based on fleet operational characteristics.
Authors: Lammert, M.; Kelly, K.; Yanowitz, J.
New EVSE Analytical Tools/Models: Electric Vehicle Infrastructure Projection Tool (EVI-Pro)
1/29/2018
This presentation addresses the fundamental question of how much charging infrastructure is needed in the United States to support PEVs. It complements ongoing EVSE initiatives by providing a comprehensive analysis of national PEV charging infrastructure requirements. The result is a quantitative estimate for a U.S. network of non-residential (public and workplace) EVSE that would be needed to support broader PEV adoption. The analysis provides guidance to public and private stakeholders who are seeking to provide nationwide charging coverage, improve the EVSE business case by maximizing station utilization, and promote effective use of private/public infrastructure investments.
Authors: Wood, E.; Rames, C. Muratori, M.
Impacts of Electrification of Light-Duty Vehicles in the United States, 2010-2017
1/25/2018
Plug-in electric vehicles (PEVs) are among the fastest growing drivetrains in the United States and worldwide. Understanding the aggregate impact of PEVs is important when exploring electricity use and petroleum consumption. This report examines the sales of PEVs in the United States from 2010 to 2017, exploring vehicle sales, electricity consumption, petroleum reduction, and battery production.
Authors: Gohlke, D.; Zhou, Y.
Life Cycle Energy and Greenhouse Gas (GHG) Emission Effects of Biodiesel in the United States with Induced Land Use Change Impacts
1/10/2018
Researchers conducted updated simulations to depict a life cycle analysis (LCA) of biodiesel production from soybeans and other feedstocks in the United States. The study addressed in detail the interaction between LCA and induced land use change (ILUC) for biodiesel. Relative to conventional petroleum diesel, soy biodiesel could achieve 76% reduction in GHG emissions without considering ILUC, or 66%-72% reduction in overall GHG emissions when various ILUC cases were considered. Soy biodiesel's fossil fuel consumption rate was also 80% lower than its petroleum counterpart. Furthermore, this study examined the cause and the implication of each key parameter affecting biodiesel LCA results using a sensitivity analysis, which identified the hot spots for fossil fuel consumption and GHG emissions of biodiesel so that future efforts can be made accordingly. Finally, researchers also investigated biodiesel produced from other feedstocks (canola oil and tallow) to contrast with soy biodiesel and petroleum diesel.
Authors: Chen, R.; Qin, Z.; Han, J.; Wang, M.; Taheripour, F.; Tyner, W.; O'Connor, D.; Duffield, J.
Notes: This Bioresource Technology article (Vol. 251 (2018): pp. 249-258) is copyrighted by Elsevier B.V. and only available by accessing it through Science Direct.
Navigation API Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles: Preprint
12/22/2017
The green routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation API route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). The navigation APIs, such Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin-destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption models are built by assigning travel from a large number of GPS driving traces to the links in TomTom MultiNet as the underlying road network layer and road grade data from a U.S. Geological Survey elevation data set. Fuel consumption on a link is calculated by the proposed fuel consumption model. This paper envisions two kinds of applications: 1) identifying alternate routes that save fuel, and 2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey GPS trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and time saving for choosing different routes is also examined for both powertrains.
Authors: Zhu, L.; Holden, J.; Gonder, J.
