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Through its Office of Planning, Budget and Analysis, the U.S. DOE Energy Efficiency and Renewable Energy (EERE) provides estimates of program benefits in its annual Congressional Budget Request. The Government Performance and Results Act (GPRA) of 1993 provides the basis for assessing the performance of Federally funded programs. Often referred to...
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... The costs estimated (Eqs. (1)-(4)) were based in several cost analysis studies which assumed large volume production scale ( [32,37,40,[45][46][47][48][49]). ...
Abstract Fuel cell powered hybrid electric vehicles (FC-HEV) and plug-in hybrid electric vehicles (FC-PHEV) are being addressed by the automotive industry as improved and more sustainable alternative technologies relatively to conventional vehicles. Nevertheless, hybrid propulsion raises new challenges in designing the vehicle powertrain. This study highlights the significance of the driving conditions and the conflict between the optimization of investment cost, efficiency and life cycle impact (LCA) in powertrain design optimization of these kinds of vehicles. A single-objective (minimization of cost, fuel or LCA CO2eq) and multi-objective genetic algorithms (minimization of the couples cost and fuel, cost and LCA CO2eq, fuel and LCA CO2eq), linked with the vehicle simulation software ADVISOR, are used to optimize the design of powertrain components. The main outcomes of the research are as follows. The optimization of LCA CO2eq emissions and cost are conflicting as well as cost and energy use, what can be observed in the Pareto solutions. The fuel and LCA CO2eq emissions optimization are coupled for pure hybrids but not for plug-in hybrid configurations, due to the electricity consumption. Fuel cell buses can reduce the energy consumption by 58%, and emit 67% less LCA CO2eq than the conventional diesel bus, and achieve compensatory payback of 0.620 $/km (depending on the hydrogen price). The FC-PHEV configuration shows more potential for achieving higher operation efficiencies, but the FC-HEV shows to have lower life cycle impact and lower cost in general.
... Its modeling capabilities and data sources have benefited from industry inputs over the years. The technical approach and vehicle simulation details can be found in peer-reviewed papers by Argonne experts, including papers based on Argonne's previous PSAT engineering model, a precursor to Autonomie that received the U.S. R&D100 Award in 2004 and featured in Rousseau et al. (2009). Autonomie can simulate vehicle technologies that are achievable in the laboratory for the years 2012, 2015, 2020, 2030, and 2045. ...
The use of alternative fuels and advanced light-duty vehicle (LDV) technologies is gaining momentum worldwide in order to reduce petroleum consumption and greenhouse gas emissions. The U.S. Department of Energy (DOE) has developed technical and cost targets at the component level for several advanced LDV technologies such as plug-in hybrid, battery electric, and fuel cell electric vehicles as well as cost targets for low-carbon fuels. DOE, Argonne National Laboratory (Argonne), and the National Renewable Energy Laboratory (NREL) recently updated their analysis of well-to-wheels (WTW) greenhouse gases (GHG) emissions, petroleum use, and the cost of ownership of vehicle technologies that have the potential to significantly reduce GHG emissions and petroleum consumption. A comprehensive assessment of how these alternative fuels and vehicle technologies options could cost-effectively meet the future carbon emissions and oil consumption targets has been conducted. This paper estimates the ownership cost and the potential reduction of WTW carbon emissions and oil consumption associated with alternative fuels and advanced LDV technologies. Efficient LDVs and low-carbon fuels can contribute to a substantial reduction in GHG emissions from the current 200–230 g/km for typical compact (small family) size diesel and gasoline vehicles. With RD&D success, the ownership costs of various advanced powertrains deployed in the 2035 time frame will likely converge, thus enhancing the probability of their market penetration. To attain market success, it is necessary that public and private sectors coordinate RD&D investments and incentive programs aiming at both reducing the cost of advanced vehicle technologies and establishing required fuel infrastructures.
... The cost for each component was estimated and used to attribute a "virtual" cost to the designed vehicle. The costs estimated in (Equations 1, 2, 3, 4) were based in several cost analysis studies which assumed large volume production scale [12,13,14,15,16,17,18]. ...
The scope of this study is to optimize the powertrain of a fuel cell powered hybrid electric vehicle and plug-in hybrid electric vehicle, aiming to minimize the cost, minimize fuel consumption, and maximixe all-electric range (AER). A genetic algorithm (GA) was used to perform single objective optimization, and a non-dominated sorting genetic algorithm (NSGA-II) to perform multi-objective optimization. Both algorithms were programmed in MATLAB. The cost, fuel consumption and AER were optimized by the GA individually, and the couples cost and fuel consumption, and cost and AER, were evaluated by the NSGA-II. In order to optimize the vehicle powertrain, not only the fuel cell, electric motor, and battery, are sized but different component models are also considered, including different battery chemistries (Lithium and Nickel-metal hydride). The battery charge sustaining level is also an optimization variable. The vehicle design is evaluated by a vehicle simulation software, ADVISOR which is connected to the optimization algorithms. The designed vehicles are simulated in a real measured driving cycle in Lisbon downtown (LisbonDt) and in the official European driving cycle NEDC. The vehicles must comply to several performance constraints, such as maximum speed, acceleration, and maximum electric range (only for plug-in vehicle). The developed methodology main objective is to present a possible best vehicle option regarding a specified objective and conditions.
... For light-duty applications, typical sizing requirements are made of four criteria: acceleration (e.g., 0 to 60 mph time), passing (30 to 50 mph time), gradeability at a given speed, and top speed [12]. The same type of requirements could be applied to tractor-trailers. ...
Hybrid electric vehicles have demonstrated their ability to significantly reduce fuel consumption for several medium-and heavy-duty applications. In this paper we analyze the impact on fuel economy of the hybridization of a tractor-trailer. The study is done in PSAT (Powertrain System Analysis Toolkit), which is a modeling and simulation toolkit for light-and heavy-duty vehicles developed by Argonne National Laboratory. Two hybrid configurations are taken into account, each one of them associated with a level of hybridization. That increases the braking energy recuperation rates. We first analyze the benefits of the two hybrid configurations on standard cycles. We then compare fuel economy results from a short standard highway cycle with a longer cruising scenario to illustrate the sensitivity of the benefits to the drive cycle. Finally, using simulation involving a grade scenario of periodical hills that we designed for this project, we show hybridization can be beneficial on hilly terrain.
... Most major forward-looking assessments of advanced vehicle efficiency technologies include massreduction as a critical component of these technology packages. For example, a number of technical studies of various levels of advanced vehicle technology include vehicle mass reductions of 14-30% (Weiss et al, Santini et al, 2001;An et al, 2001;Graham et al, 2001;Lipman and Delucchi, 2006;Cheah et al, 2007;Pagerit et al, 2006;DeLorme et al, 2009). ...
Past automotive trends, ongoing technology breakthroughs, and recent announcements by automakers make it clear that reducing the mass of automobiles is a critical technology objective for vehicle performance, carbon dioxide (CO2) emissions, and fuel economy. Vehicle mass-reduction technology offers the potential to reduce the mass of vehicles without compromise in other vehicle attributes, like acceleration, size, cargo capacity, or structural integrity. As regulatory agencies continue to assess more stringent CO2 and fuel economy standards for the future, it is unclear the exact extent to which vehicle mass-reduction technology will be utilized alongside other efficiency technologies like advanced combustion and hybrid system technology. This report reviews ongoing automotive trends, research literature, and advanced concepts for vehicle mass optimization in an attempt to better characterize where automobiles – and their mass in particular – might be headed.
System design tools including simulation and component optimization are an increasingly important component of the vehicle design process, placing more emphasis on early stages of design to reduce redesign and enable more robust design. This study focuses on the energy use and power management simulations used in vehicle design and optimization. Vehicle performance is most often evaluated in simulation, physical testing, and certification using drive cycle cases (also known as dynamometer schedules or drive schedules). In vehicle optimization studies, the information included in each drive cycle has been shown to influence the attributes of the optimized vehicle, and including more drive cycles in simulation optimizations has been shown to improve the robustness of the optimized design. This paper aims to quantitatively understand the effect of drive cycles on optimization in vehicle design and to specify drive cycles that can lead to robust vehicle design with minimal simulation. Two investigations are performed in service of this objective; investigation 1 tests how different combinations of drive cycles affect optimized vehicle performance and design variables (DV); investigation 2 evaluates the use of stochastic drive cycles for improving the robustness of vehicle designs without adding computational cost to the design and optimization process.
Der Bericht stellt die Ergebnisse einer Untersuchung der mittel- bis langfristigen (bis 2050) energiewirtschaftlichen Perspektiven der Elektromobilität (Plug-In-Hybridfahrzeuge und Elektrofahrzeuge) in einem Versorgungssystem mit hohem Anteil fluktuierender erneuerbarer Energien dar. In Szenarien wurden die Voraussetzungen für eine erfolgreiche Technologie- und Marktentwicklung von Plug-In-Hybrid- und Elektrofahrzeugen analysiert. Aus heutigen realen Fahrprofilen wurden Nutzerprofile und die daraus resultierende Stromnachfrage und Batteriekapazität der Fahrzeugflotte am Netz in stündlicher Auflösung berechnet. Daraus konnte unter plausiblen Rahmenannahmen eine für den Stromausgleich im Energiesystem zur Verfügung stehende Batteriekapazität ermittelt und in ein volkswirtschaftlich kostenoptimierendes Energiesystemmodell integriert werden. Mit diesem Modell wurden dynamische Simulationen der Lastdeckung durch erneuerbare Energien, regelbare KWK- und Kondensationskraftwerke, stationäre und mobile Stromspeicher sowie den weiträumigen Stromtransport im transeuropäischen Stromverbund durchgeführt. Die Szenarienanalysen zeigen das Potenzial einer gesteuerten Ladung der Fahrzeugbatterien zum Stromausgleich im Vergleich zu anderen Ausgleichsoptionen. In weiteren modellbasierten Untersuchungen wurden die Realisierbarkeit der netztechnischen Integration der Elektromobilität und hierbei Synergieeffekte mit dezentralen Stromerzeugern auf den Ebenen Hausenergieversorgung, Verteilnetz und Übertragungsnetz analysiert und Auswirkungen auf den Netzausbaubedarf untersucht.
The scope of this study is to optimize the component sizing of a fuel cell powered PHEV (PHEV-FC), using a genetic algorithm (GA) to optimize component cost for a typical urban taxi fleet usage. A simplified heuristic methodology is the first approach for the PHEV design. Cost functions for the components are estimated as well as specific power functions to perform the vehicle component sizing and cost evaluation. The used GA aims to optimize the cost of the designed vehicle and evaluate performance constrains (maximum speed and acceleration, electric range, overall performance) using an external tool, a vehicle simulator software ADVISOR, automated with the algorithm (in loop). A real measured driving cycle and official New European Driving Cycle (NEDC) are used for the vehicle simulations. Different fuel cells, motor and battery and a range of battery module number are the input data for the GA optimization regarding component selection. The initialization of the heuristic method relays on the vehicle specific power (VSP) methodology, namely on maximum power requirements of the specified driving cycle. It assumes efficiencies and main characteristics) of the components to perform an iterative calculation, followed by a trial and error evaluation. The GA is capable to tune the component sizing to the respective performance requirements. It can be seen that the cost may not have a direct relation with the consumption, since that different components lead to different vehicle weight and performance. An important limitation of the current methodology is that the vehicle optimization is fully dependent on the assigned driving cycle and performance constrains. Input data and GA parameter tuning deserves exhaustive work to achieve more precise results. The heuristic method although very fast to achieve results lacks sensitiveness regarding the proposed constraints to the design, since the evaluation process is made after the design. The GA allows adjusting better solutions to the requirements of the driving cycle and constraints, and independently selecting the fuel cell, motor and battery. Both heuristic and GA method results are compared with a conventional diesel taxi vehicle (ICEV). The designed PHEV-FC with the lowest cost and compliant with the requirements resulted from the GA method and was powered by a 24 kW fuel cell, a 130 kW motor, and a 251, 17 kWh Li-ion battery pack. Using the real Lisbon downtown driving cycle, the optimized PHEV-FC achieved a 2.1 MJ/km daily taxi service, which represents less 18 % of energy consumption than the ICEV taxi. The best results produced for the PHEV design regarding the real driving cycle have 67 % higher energy consumption and are 80 % more costly than NEDC, since NEDC it is a less demanding cycle.
