The high vehicle turnover, large fleet size, and time-sensitive charging requirement at centralized facilities make electrified rental car fleets a potentially significant source of electricity demand. Yet the energy and infrastructure implications of rental fleet electrification remain largely understudied in both industry and academia. To address this gap, this study introduces EVI-Rental, a scalable discrete-event simulation model that quantifies the impacts of rental fleet electrification on electricity demand, charging infrastructure, customer experience, and life-cycle costs. A case study of the consolidated rental car facility at Dallas-Fort Worth International Airport demonstrates the model's capabilities by evaluating strategies such as state-of-charge policies at pickup and return, solar and battery storage integration, and charging station design. Using customizable inputs informed by rental car services, EVI-Rental can be adapted to diverse operations, providing a decision-support tool for individual rental car companies and airport consolidated rental car facilities planning cost-effective transitions to electric fleets.

In this work, the integrated design and dispatch of behind-the-meter or distributed resources (e.g. stationary battery storage and solar PV generation) is considered. A simulation-based framework is employed, generating high-fidelity results with closed-loop predictive control at a fine resolution, at the expense of high computational cost (several minutes to a few hours per design point). To address this challenge, parallel derivative-free design methods are considered. Four methods are compared, including state-of-the-art surrogate-based methods (Radial-Basis Functions and Gaussian processes) and sampling strategies, an evolutionary-based method, and a simple sequential grid refinement method. As a case study, two types of design problem with increasing complexity are considered, namely, the design of behind-the-meter resources (three design variables) and the inclusion of grid capacity (four design variables). The second yields a constrained design problem for which violations can only be determined after solving the computationally expensive simulation. For the three-dimensional case, all methods present a good performance, achieving a solution within 1% of the optimum after the first iteration, with the sequential grid refinement exhibiting the fastest convergence and achieving the best final objective value. This indicates that the parallel evaluation of multiple sampling points may be more important than the choice of method for small decision spaces. For the four-dimensional constrained case, the Genetic Algorithm presents the best tradeoff between performance and computational effort, while the rough objective function terrain generated by constraint violation penalties reduces the performance of surrogate-based methods. Contour plots with flat regions indicate flexibility in the optimal design and highlight the importance of characterizing the solution space.

Electrification of rental car centers at major airports is expected to generate tens of MW in additional power loads. The magnitude of these loads poses challenges including high utility costs, expensive and lengthy distribution capacity upgrades, and disruptions to traditional operation. Behind-the-meter stationary battery storage and onsite photovoltaic generation offer a viable solution to these challenges without impacting the operation and business model of rental car companies, defined by minimal fleet inventory and short vehicle dwell time. Using data-driven syn-thetic charging loads for the rental car center at the Dallas/Fort Worth airport in the United States, we show that optimally-designed and controlled behind - the- meter resources can reduce the lifecycle cost of electrified rental centers by an average 41 % and reduce peak grid demand by 64 %, deferring the need for distribution upgrades or potentially avoiding it altogether.

Airport rental car facilities present a significant potential for decarbonization through the adoption of electric vehicles. This transition will likely be accompanied by the deployment of fast charging infrastructure, necessary for keeping vehicle inventory and dwell times low. In this context, distributed or behind-the-meter resources such as stationary battery storage and PV generation are proposed as a solution for reducing costs and improving resiliency. The impact of load uncertainty on the optimal control and design of these systems, however, is a critical topic that has been less explored. In this work, the control and design of behind-the-meter resources under load uncertainty from a large-scale electrified rental car facility is investigated. An Economic Model Predictive Control model is presented, along with stochastic extensions. Different control policies and forecasting methods are compared, demonstrating that chance constraints can be employed to improve performance with limited forecasting. System design is shown to significantly impact control performance, indicating that larger batteries are necessary for less optimal policies. Finally, the effect of control policy choice on the optimal system design is evaluated.

A predictive control/scheduling optimization model is proposed for managed charging of an electric vehicle (EV) fleet under time-of-use energy and demand prices, high vehicle utilization frequency (short dwell times), multiple charge- acceptance curves (configurable charging rates), and flexible vehicle demand. This context is particularly relevant for flight schools (small electric aircraft) or other commercial facilities where an EV fleet performs multiple operating and fast-charging sessions on the same day. The proposed model performs both the operational and charging scheduling of the vehicles, which is not typically done for residential managed charging and significantly increases problem complexity. The problem is formulated as a MILP model and a case study of a small fast-charging station is presented. Results demonstrate a significant reduction in operating cost, mainly from peak shaving during high demand price periods, achieved by coordinating the operation of different vehicles, chargers and charging rates.

The growing electrification of buildings and vehicles, while a natural step towards achieving global decarbonization, poses some challenges for the electric grid in terms of power consumption. One way of addressing them is by deploying onsite, behind-the-meter resources (BTMR), such as battery energy storage and solar PV generation. The optimal design of these systems, however, is a demanding task that depends on the integration of multiple complex subsystems. In this work, the optimal integrated design and dispatch of BTMR systems for retail buildings with electric vehicle fast charging stations is addressed. A framework is proposed, combining high-fidelity simulation (of buildings, electric vehicle fast charging stations, and BTMR), predictive control strategies with closed-loop implementation, and a derivative-free design method that explores parallelization and high-performance computing. Focus is given to the design layer, highlighting the effect of parallelization on the choice of the method, computational effort, and types of results. A case study of a big-box grocery store with an EV fast charging station is presented, and its optimal BTMR system is identified in terms of equipment sizes, costs (capital, utility, lifecycle, and levelized) and resiliency against outages, demonstrating great potential for real-world applications.

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. This study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $0.035/kWh (6%), with an average of $0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

As electric vehicle penetration increases, charging is expected to have a significant impact on the grid. Electric vehicle charging stations will greatly affect a building site's power demand, especially with the onset of fast charging with power levels as high as 350 kW per charger. Here, we assess how electric vehicle charging stations would impact a retail big box grocery store, exploring numerous station sizes, charging power levels, and utilization factors in various climate zones and seasons. We measure the effect of charging by assessing changes in monthly peak power demand, electricity usage, and annual electricity bill, computed using three distinct rate structures. We find that an electric vehicle station has the potential to dwarf a big box building's power demand if behind the same meter, increasing monthly peak power demand at the site by over 250%. Cold-climate areas paired with rate structures incorporating high demand charges are most susceptible for significant changes to the annual electricity bill, with increases as high as 88%.

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

Fuel cells have emerged as viable solutions in areas such as stationary and backup power systems, material handling equipment (MHE), and fuel cell electric vehicles (FCEV). Persistent challenges for fuel cells and electrolyzers include high initial cost and the availability of hydrogen infrastructure to support FCEV and MHE fleets. Cost of fuel cells are still high compared to other power generation systems such as diesel and natural gas generators. This, however, can be linked to two facts: first is low production volumes generally and second is emerging manufacturing technologies currently in R&D that need to be scaled up to factory production volumes. This study investigates current manufacturing processes used in production of fuel cells (e.g., spray coating and manual assembly) and emerging manufacturing technologies (e.g., roll-to-roll catalyst coating) to investigate key cost drivers and potential cost reductions in manufacturing of fuel cells and electrolyzers. In particular, we focus on how cost reductions for advance manufacturing technologies may be more significant at scale than existing technologies.

This report provides the analysis results of agricultural and preprocessing equipment and manufacturing requirements to support the mobilization of the Billion Ton projections utilizing the conventional supply chain logistics. This report discusses the number of required agricultural machinery and their market values, the drivers and barriers of the transition in agricultural equipment, the potential economic impacts to the US associated with expansion of these equipment manufacturing and the factors that impact the transition in agricultural machinery to support the growth of a large-scale biofuel and bio-products industry in short and long terms. Short and long terms refer to the early development and mature development phases of the cellulosic biofuels industry in the United States.

Fuel cell electric vehicles (FCEVs) have now entered the market as zero-emission vehicles. Original equipment manufacturers such as Toyota, Honda, and Hyundai have released commercial cars in parallel with efforts focusing on the development of hydrogen refueling infrastructure to support new FCEV fleets. Persistent challenges for FCEVs include high initial vehicle cost and the availability of hydrogen stations to support FCEV fleets. This study sheds light on the factors that drive manufacturing competitiveness of the principal systems in hydrogen refueling stations, including compressors, storage tanks, precoolers, and dispensers. To explore major cost drivers and investigate possible cost reduction areas, bottom-up manufacturing cost models were developed for these systems. Results from these manufacturing cost models show there is substantial room for cost reductions through economies of scale, as fixed costs can be spread over more units. Results also show that purchasing larger quantities of commodity and purchased parts can drive significant cost reductions. Intuitively, these cost reductions will be reflected in lower hydrogen fuel prices. A simple cost analysis shows there is some room for cost reduction in the manufacturing cost of the hydrogen refueling station systems, which could reach 35% or more when achieving production rates of more than 100 units per year. We estimated the potential cost reduction in hydrogen compression, storage and dispensing as a result of capital cost reduction to reach 5% or more when hydrogen refueling station systems are produced at scale.

This study sheds light on current and future recycling methods for spent Li-ion batteries from retired vehicles. The demands of Li-ion batteries for automotive applications and power electronics are expected to increase significantly in the next 15-20 years. Recycling cathode materials from end-of-life batteries provides a sustainable source of materials, and offers an economic alternative for some of the high value elements such as cobalt and nickel. Insights and directions for future R&D will be presented in this paper based on the results of the supply chain and techno-economic analyses made for end-of-life li-ion batteries.

Lithium ion batteries (LIB) continue to gain market share in response to the increasing demand for electric vehicles, consumer electronics, and energy storage. The increased demand for LIB has highlighted potential problems in the supply chain of raw materials needed for their manufacture. Some critical metals used in LIB, namely lithium, cobalt, and graphite are scarce, are not currently mined in large quantities, or are mined in only a few countries whose trade policies could limit availability and impact prices. The environmental and social impacts of mining these materials have also drawn attention as production ramps up to meet the increased demand. Closed-loop systems with recycling at the end-of-life provide a pathway to lower environmental impacts and a source of high value materials that can be used in producing new batteries. Because environmental regulations concerning end-of-life batteries are not fully developed or implemented, most of these batteries currently end up in the landfills, with a very small number of spent batteries sent to the existing recycling facilities. However, with proactive regulations, an increasing supply of spent batteries, and innovations in recycling technologies, end-of-life batteries could supply a significant fraction of the materials needed for manufacturing of new LIB. This paper reviews the current state of the LIB manufacturing supply chain, addresses some issues associated with battery end-of-life, and sheds light on the importance of LIB recycling from the environmental and value chain perspectives. We also discuss the expected benefits of recycling on the global LIB supply chain.

The Clean Energy Manufacturing Analysis Center (CEMAC), sponsored by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE), provides objective analysis and up-to-date data on global supply chains and manufacturing of clean energy technologies. Benchmarks of Global Clean Energy Manufacturing sheds light on several fundamental questions about the global clean technology manufacturing enterprise: How does clean energy technology manufacturing impact national economies? What are the economic opportunities across the manufacturing supply chain? What are the global dynamics of clean energy technology manufacturing?

This report documents the CEMAC methodologies for developing and reporting annual global clean energy manufacturing benchmarks. The report reviews previously published manufacturing benchmark reports and foundational data, establishes a framework for benchmarking clean energy technologies, describes the CEMAC benchmark analysis methodologies, and describes the application of the methodologies to the manufacturing of four specific clean energy technologies.

Wind power is an inexhaustible form of energy that is being captured throughout the U.S. to power the engine of our economy. A robust, domestic wind industry promises to increase U.S. industry growth and competitiveness, strengthen U.S. energy security independence, and promote domestic manufacturing nationwide. As of 2016, ~82GW of wind capacity had been installed, and wind power now provides more than 5.5% of the nation’s electricity and supports more than 100,000 domestic jobs, including 500 manufacturing facilities in 43 States. To reach the U.S. Department of Energy’s (DOE’s) 2015 Wind Vision study scenario of wind power serving 35% of the nation's end-use demand by 2050, significant advances are necessary in all areas of wind technologies and market. An area that can greatly impact the cost and rate of innovation in wind technologies is the use of advanced manufacturing, with one of the most promising areas being additive manufacturing (AM). Considering the tremendous promise offered by advanced manufacturing, it is the purpose of this report to identify the use of AM in the production and operation of wind energy systems. The report has been produced as a collaborative effort for the DOE Wind Energy Technology Office (WETO), between Oak Ridge National Laboratory (ORNL) and the National Renewable Energy Laboratory (NREL).