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Smart J.,Idaho National Laboratory | Schey S.,ECOtality North America
SAE International Journal of Alternative Powertrains

In 2010, a large-scale plug-in electric vehicle (PEV) infrastructure demonstration was launched to deploy an unprecedented number of PEVs and charging infrastructure. This demonstration, called The EV Project, is funded by the U.S. Department of Energy and led by ECOtality North America. ECOtality has partnered with Nissan North America and General Motors to deploy up to 8,300 Nissan LEAF™ battery electric vehicles and Chevrolet Volt extended-range electric vehicles, along with approximately 14,000 AC Level 2 and DC fast-charging units in 18 metropolitan areas across the United States. ECOtality and the Idaho National Laboratory partnered to collect and analyze electronic data from EV Project vehicles and charging units. An early analysis of data from Nissan LEAFs enrolled in The EV Project was performed. The data set analyzed came from 2,903 privately owned vehicles, which logged over 10 million driving miles in 2011. On average, Nissan LEAF drivers drove 6.9 miles per trip and 30.3 miles per day. Median values were 4.0 and 26.8 miles, respectively. In environments without many public charging locations, LEAF drivers averaged 28.8 miles between consecutive charging events, with a median of 27.1miles. The average and median number of times vehicles were charged per day driven were 1.05 and 0.99 charging events per day, respectively. Analysis of charging location determined that 82% of charging events were conducted at the project participants' homes using their residential electric vehicle supply equipment. 18% of charging events were performed elsewhere. Despite the relatively low numbers of publicly available charging units, over 70% of vehicles were charged away from home. Most of those vehicles charged at many distinct locations, such as shopping centers, health clubs, restaurants, and business offices. Some of the most frequently and infrequently charged vehicles were charged exclusively at home or in public, but most supplemented home charging with away-from-home charging. Copyright © 2012 SAE International. Source

Secanell M.,University of Alberta | Wishart J.,ECOtality North America | Dobson P.,University of Alberta
Journal of Power Sources

The design of fuel cells is a challenging endeavour due to the multitude of physical phenomena that need to be simultaneously optimized in order to achieve proper fuel cell operation. Fuel cell design is a multi-objective, multi-variable problem. In order to design fuel cells by computational design, a mathematical formulation of the design problem needs to be developed. The problem can then be solved using numerical optimization algorithms and a computational fuel cell model. In the past decade, the fuel cell community has gained momentum in the area of numerical design. In this article, research aimed at using numerical optimization to design fuel cells and fuel cell systems is reviewed. The review discusses the strengths, limitations, advantages, and disadvantages of optimization formulations and numerical optimization algorithms, and insight obtained from previous studies. © 2010 Elsevier B.V. All rights reserved. Source

Smart J.,Idaho National Laboratory | Powell W.,Idaho National Laboratory | Schey S.,ECOtality North America
SAE Technical Papers

ECOtality North America, OnStar, and the Idaho National Laboratory have partnered to collect and analyze electronic data from Chevrolet Volts enrolled in The EV Project, which is a large-scale plug-in electric vehicle infrastructure demonstration being conducted in 21 metropolitan areas across the United States. This paper presents results of an early analysis of these data. The data set analyzed came from 923 privately owned vehicles, which logged over 4.7 million driving miles from October 2011 to October 2012. These data are used to identify the potential of electric vehicle (EV) mode driving, based on driver and charging behavior. Driving and charging behavior is quantified with metrics such as daily vehicle miles traveled, number of charging events performed per day, and distance driven between consecutive charging events. Drivers averaged 40.7 miles per day, with a median of 31.6 miles per day. Vehicles were charged 1.46 times per vehicle day driven on average, with a median of 1 charging event per day driven. This results in an average of 27.9 miles between consecutive charging events and a median distance of 19.8 miles between charging events. Underlying distributions for these metrics also are examined to find a wide variation in driving and charging behavior across vehicles and vehicle days. Overall, 81% of the vehicles averaged 40 miles or less between consecutive charging events. Assuming a fixed EV mode range of 35 miles, vehicles in this study had the potential to drive 73% of their miles in EV mode. These results show that Chevrolet Volt drivers participating in The EV Project found frequent opportunities to charge their vehicles, such that a high percentage of their driving was performed in EV mode. Also, drivers took advantage of their vehicle's extended range mode to meet their driving needs beyond the all-electric range of their vehicle. Source

Lidicker J.,University of California at Berkeley | Sathaye N.,ECOtality North America | Madanat S.,University of California at Berkeley | Horvath A.,University of California at Berkeley
Journal of Infrastructure Systems

In recent decades, pavement management optimization has been designed with the objective of minimizing user and agency costs. However, recent analyses indicate that pavement management decisions also have significant impacts on life-cycle greenhouse gas (GHG) emissions. This study expands beyond minimization of life-cycle costs to also include GHG emissions. Previous work on the single-facility, continuous-state, continuous-time optimal pavement resurfacing problem is extended to solve the multicriteria optimization problem with the two objectives of minimizing costs and GHG emissions. Results indicate that there is a trade-off between costs and emissions when developing a pavement resurfacing policy, providing a range of GHG emissions reduction cost-effectiveness options. Case studies for an arterial and a major highway are presented to highlight the contrast between policy decisions for various pavement and vehicle technologies. © 2013 American Society of Civil Engineers. Source

Carlson R.B.,Idaho National Laboratory | Lohse-Busch H.,Argonne National Laboratory | Diez J.,ECOtality North America | Gibbs J.,U.S. Department of Energy
SAE International Journal of Alternative Powertrains

The U.S. Department of Energy's Office of Energy Efficiency & Renewable Energy initiated a study that conducted coastdown testing and chassis dynamometer testing of three vehicles, each at multiple test weights, in an effort to determine the impact of a vehicle's mass on road load force and energy consumption. The testing and analysis also investigated the sensitivity of the vehicle's powertrain architecture (i.e., conventional internal combustion powertrain, hybrid electric, or all-electric) on the magnitude of the impact of vehicle mass. The three vehicles used in testing are a 2012 Ford Fusion V6, a 2012 Ford Fusion Hybrid, and a 2011 Nissan Leaf. Testing included coastdown testing on a test track to determine the drag forces and road load at each test weight for each vehicle. Many quality measures were used to ensure only mass variations impact the road load measurements. Chassis dynamometer testing was conducted over standard drive cycles on each vehicle at multiple test weights to determine the fuel consumption or electrical energy consumption impact caused by change in vehicle mass. The road load measurements obtained from the coastdown testing were used to configure the chassis dynamometer. Chassis dynamometer testing also incorporated many quality controls to ensure accurate results. The results of the testing and analysis showed that for a given vehicle, the road load shows a slightly non-linear trend of decreasing road load with decreasing mass. This trend appears to be consistent across vehicle powertrain architectures (i.e., conventional powertrain, hybrid electric, or all-electric). Chassis dynamometer testing of fuel consumption or electrical energy consumption showed for the Highway Fuel Economy Test drive cycle there was little impact due to change in mass for all three vehicles. For the Urban Dynamometer Drive Schedule and US06 drive cycle, there was a 2.4 to 4.1% change in energy consumption for a 10% change in mass. Additionally, the less efficient the vehicle's powertrain, the larger the energy consumption benefits were for mass reduction. Copyright © 2013 SAE International. Source

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