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Overview of the development of algebraic battery lifetime modeling efforts within NREL's Electrochemical Energy Storage group within the Energy Conversion and Storage Systems Center. Traditional approaches to developing battery lifetime models are compared with a new methodology incorporating machine learning to autonomously identify parsimonious model equations.
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A Unitized Regenerative Fuel Cell (URFC) Energy Storage System is being developed at the NASA Glenn Research Center. This URFC system is unique in that it uses regenerative gas dryers/humidifiers that are mounted on the surface of the gas storage tanks that act as the radiators for thermal control of the URFC system. As the gas storage tanks cool down during URFC charging the regenerative gas dryers/humidifiers dry the hydrogen and oxygen gases produced by electrolysis. As the gas storage tanks heat up during URFC discharging, the regenerative gas dryers/humidifiers humidify the hydrogen and oxygen gases used by fuel cell. An analytical model was developed to simulate the URFC system's regenerative gas dryers/humidifiers. The model is in the form of an EXCEL@ worksheet that allows the investigation of the regenerative gas dryers/humidifier performance as a function of time and position within the regenerative gas dryers/humidifiers, as well as other key system variables. Finite Element Analysis (FEA) modeling of the regenerative gas dryers/humidifier and gas storage tank wall was also done to analyze spatial temperature distribution within the regenerative gas dryers/humidifiers and the localized tank wall. Test results obtained from the testing of the regenerative gas dryers/humidifiers in a thermal vacuum environment were used to corroborate the analyses.
A Unitized Regenerative Fuel Cell (URFC) Energy Storage System is being developed at the NASA Glenn Research Center. This URFC system is unique in that it uses the surface area of the hydrogen and oxygen storage tanks as radiating heat surfaces for overall thermal control of the system. The tank surfaces also play an important role in the temperature control of regenerative gas dryers/humidifiers used to dry the hydrogen and oxygen gases produced by electrolysis during the charging and also used to humidify the hydrogen and oxygen gases used by fuel cell during the discharging of the URFCS. A bi- directional pressure controller is used to control the pressure of the oxygen and hydrogen gas inside the URFC stack during both charging and discharging of the URFC system. A water storage accumulator is used to store water reactant and control water pressure inside the URFC stack.
Successful widespread commercialization of electric vehicles is contingent upon development of safe high energy density batteries capable of long cycle life. Lithium metal affords the highest theoretical capacity (3,860 mAh/g) and lowest electrochemical potential (-3.04V vs SHE), which offers the highest specific energy density of anode materials today. While Li-ion batteries are capable of delivering energy densities of 400-600 Wh/kg, the development of lithium metal batteries such as Li-S and Li-air may boost this number up to 650 and 950 Wh/kg, respectively. However, significant progress towards the passivation of lithium metal must occur before the energy density benefit can be realized. Issues with lithium dendrite formation, anode volume expansion, and continuous solid electrolyte interphase (SEI) build-up often result in significant safety concerns, high cell resistance, and poor cycle life. The intrinsic high reactivity between lithium metal with conventional lithium ion electrolytes (organic carbonate-based solvents) makes it extremely difficult to overcome these problems. In this project, Wildcat performed focused, fundamental research and development on composite polymer/ceramic electrolytes and protected lithium metal anodes to develop an all solid state lithium metal battery targeting the DOE requirements to enable commercialization. Wildcat successfully leveraged its high throughput battery platform and explored a broad composite electrolyte compositional space. Additionally, Wildcat screened large numbers of inorganic and organic coatings for lithium metal protection using in situ liquid methods and translated the best results to all solid cells. The proposed composite polymer/ceramic electrolyte and a protected lithium metal anode enabled a solid-state lithium metal battery. The ultimate targets from this effort will deliver a safe all solid-state lithium metal pouch cell with over 350 Wh/kg and over 1,000 cycles (C/3) with the cost estimate below $100/kWh. During the course of the project, we demonstrated stable cycling of 10’s of cycles with the composite SSE and our protected lithium. The work has continued after the conclusion of the DOE funding, demonstrating further improvements in catholyte composition and thinner composite solid electrolytes.
Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methods, coupled with functional and synthetic genomics, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require a detailed systems biology understanding of photosynthetic processes and cellular metabolism. Genome-scale metabolic models (GEMs) are at the core of systems analysis of cellular processes and form a common organizational framework for analyses of data resulting from functional genomics experimental work and computational studies. Therefore, there is a clear demand for high quality photosynthetic model organisms and the appropriate computational tools that enable systems analysis of light-driven metabolism. Through research conducted we expanded the currently available repertoire of photosynthetic GEMs to include the commercially valuable model diatom Phaeoctylum tricornutum. Diatoms have a peculiar and distinct evolutionary footprint and represent a major eukaryotic lineage that is taxonomically and functionally distinct from green and red algae and vascular plants. Therefore, the true potential for light-driven metabolism aimed at biofuel production remains poorly understood at a systems level for a large subset of the global diversity of photosynthetic organisms. The metabolic capabilities of P. tricornutum were comparatively modeled with those from other photosynthetic groups in order to elucidate the occurrence of metabolic traits within and between phototrophs. Additionally, this research resulted in significant extension of the COnstraints Based Reconstruction and Analysis (COBRA) Toolbox to accommodate the crucial need for infrastructure required for ‘omics data integration and analysis in the context of genome-scale models. Therefore, the proposed research achieved two important goals. First, within the broad scope of photosynthetic organisms, we functionally compared and, as a result, identified cellular processes that require optimization in order to enable deployment as biofuel feedstock. Second, the proposed research resulted in development of key computational infrastructure, which can be further extended to other biological systems, that is currently lacking but necessary for multiple ‘omics data integration.
This fact sheet describes the development and release of a high-fidelity solar time series data set for the entire Southeast Asia region, which can be used to drive energy sector investment, support enabling policies to support solar energy market expansion, and inform prospecting, renewable energy auctions, target-setting, grid integration and other high impact decisions, ranging from the micro to macro scale, to accelerate renewable energy deployment throughout SE Asia. This data is available on the enhanced and expanded RE Data Explorer (RE Explorer) platform.
Solar thermal technologies are a promising technology to supply low-cost thermal energy to industrial processes, but there are often significant barriers to entry to industrial owners considering these technologies for their energy demands. To overcome this barrier and convey economic value to customers, NREL and Sandia National Laboratories developed Solar Thermal Energy Planner (STEP 1), a new web-based decision support tool for solar industrial process heat systems. At SolarPACES 2024, the STEP 1 tool was still under development; progress, methodologies, and a preliminary case study was presented. With the STEP 1 tool launch in May 2025, in this work, the initial version of the full public tool will be presented with demonstrations of its capabilities using a few case studies. First, the user's process heat needs such as location, process media (e.g., steam, air), process temperature, land availability, electricity and fuel costs, among other parameters. STEP 1 features a mapping interface that allows users to draw land and roof boundaries. The process media and temperature inform technology selection criteria modules that determine the appropriate solar thermal collection technologies, as well as congruent heat transfer media (e.g., hot water, oil, salt). Once the solar thermal technology selected, its nominal thermal production for the given site is characterized using NREL's System Advisor Model (SAM). Then, a modified version of NREL's REopt optimal sizing and dispatch optimization tool determines cost-optimal sizing. Within minutes, the user receives the results of the technoeconomics analysis, including the size and performance of the cost-optimal solar-plus-storage system. The cost of the system is compared to business-as-usual (e.g., an existing, standalone natural gas boiler). Users can download key results to store for sensitivity analyses. Examples of flat plate collector, parabolic trough, and molten salt tower applications with and without PV hybridization for different industrial facility types are presented in this work. The STEP 1 tool aims to reduce barriers to the adoption of solar heating solutions stemming from a lack of familiarity and technical background with solar system design options and costs among industry stakeholders.
A Machine Learning-based predictive model is developed for optimal dispatching energy storage system integrated with Electric Vehicle battery charger. The model is effectively trained using transfer learning algorithm and successfully validated via measurement data to achieve high fidelity and accuracy. The study findings provide possibilities for future development of the presented approach to implement computational heavy predictive algorithms with multi-step predictions in emerging modular bidirectional charger topologies to realize vehicle-to-grid (V2G) technology. The online tuning can be explored using the new measurements obtained during the operation, henceforth, enhancing the controller performance for integrating multiple DERs into distribution power networks that shows huge interest for the utilities and regulatory agencies. Multiple operation scenatios have been evaluated and simulation results are discussed to verify intended performance of the proposed control solution.
This slide deck was developed for and presented at an Energy Fundamentals Course hosted by the Bangladesh University of Engineering and Technology (BUET) in October 2022. The National Renewable Energy Laboratory (NREL) helped organize this course in partnership with the United States Agency for International Development (USAID). The students in this four-day course were postgraduates and working professionals in the energy sector or related industries in Bangladesh. While some of the content in the slide deck is tailored to Bangladesh specifically, this presentation is intended to be a general primer on energy storage that can be utilized for similar purposes by other universities or organizations throughout the world. The content of this slide deck is not intended to be fully comprehensive of all energy storage concepts.
The two paths for documenting compliance with ANSI/ASHRAE/IES Standard 90.1 are the prescriptive path and the performance path. Beyond code programs and rating systems (for example, USGBC-LEED ) are primarily known to use a third path – the Appendix G Performance Rating Method. An update in the 2016 edition of Standard 90.1 approved the Appendix G Performance Rating System for code compliance, extending its application and allowing for greater consistency of modeling rules for code and beyond code building energy modeling. The Appendix G PRM provides rules for the development of whole building energy models of baseline and proposed models for calculating the “performance cost index target” value using the simulated energy results of the baseline and proposed models and the building performance factors published in Table 4.2.1.1 of the Standard. This report documents (1) the methodology used for development of the baseline and proposed energy models of the Pacific Northwest National Laboratory and U.S. Department of Energy commercial building prototypes using the Appendix G Performance Rating Method; and (2) the building performance factors that were calculated using those models.
Clean energy research and development (R and D) leading to commercial technologies is vital to economic development, technology competitiveness, and reduced environmental impact. Over the past 30 years, such efforts have advanced technology performance and reduced cost by leveraging network effects and economies of scale. After demonstrating promise in applied R and D, successful clean energy and energy efficiency technologies are incorporated into an initial product sold by the private sector. Despite its importance, processes by which first commercialization occurs are difficult to generalize while capturing specific insights from practitioners in markets and technologies. This paper presents a policy-focused qualitative assessment of the first commercialization of four diverse energy technologies: thin film photovoltaics, wind turbine blades, dual-stage refrigeration evaporators, and fuel cells for material handling equipment. Each technology presents distinct value propositions, markets, and regulatory drivers. The case studies indicate three common characteristics of successful first commercialization for new energy technologies: 1) good fit between the technology, R&D infrastructure, and public-private partnership models; 2) high degree of alignment of government regulations and R&D priorities with market forces; and 3) compatibility between time scales required for R&D, product development, and opportunities. These findings may inform energy investment decision-making, maximize benefits from R&D, and advance the transition to a low-emission future.
Abstract Anionic redox chemistry is emerging as a key concept in the development of high‐energy lithium‐ion batteries, as it enables a nearly doubled charge storage capacity, aiding the development of high‐capacity batteries. However, the anionic reactivity is frequently irreversible from charge to discharge, leading to rapid decay of the capacity and voltage of batteries over long‐term cycling. Although the possibility of controlling the anionic redox reactions by tuning the geometric and electronic structures has recently been proposed, the implementation of this strategy is still a critical challenge. Herein, a strategy is proposed to improve the anionic redox reversibility of a model anionic redox active cathode material, Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 , by tuning the surface ligand geometry via the growth of a lattice‐compatible spinel LiCoO 2 coating layer on the particle surface. Detailed local structure and first principles investigations reveal that the shape and orientation of the octahedral layer in the host lattice are modified. Accordingly, a two‐band oxygen redox behavior is triggered in the ligand‐orientation‐regulated Li‐rich cathode, leading to enhanced reversibility, and thus, remarkably improved capacity and voltage retention over cycling. This study highlights the importance of controllable ligand orientation, carving a new path for the development and design of Li‐rich cathodes in the future.
Accessibility in a geo-spatial context refers to the ease of reaching a variety of opportunities from a given location. Accessibility theories (and resulting metrics) have traditionally focused on quantifying access to specific opportunities (such as jobs), or focused on specific modes (such as car, bike, etc.). Such approaches often fall short of providing a comprehensive picture of the true accessibility potential of a location as a combination of multiple modes to multiple types of destinations. Addressing this drawback, a novel metric labeled the ‘Mobility Energy Productivity (MEP) Metric’ was developed at the National Renewable Energy Laboratory to quantify the mobility potential of a location to connect people to goods, services, and employment using a variety of modes, while accounting for time, energy, and affordability. The MEP metric has been integrated with advance travel behavior models to compute the changes in mobility potential for various future scenarios, such as introduction of automated vehicles, and/or electric vehicles—but does so at the aggregate, or average-citizen level. The MEP in its initial iteration is not customized to the particular socio-economic contingents, or even to an individual whose modal availability or pattern of trip making may substantially differ from the average. Addressing this gap, this research effort extends the MEP framework from a static state to a more tailored and dynamic state, one in which an individual, or group can customize the metric for their unique characteristics, such as modes, activity patterns, and time-of-day preferences. The extended MEP metric framework can now be integrated to assess the customized mobility energy productivity of an individual.
A solar absorption cooling system consisting of a flat plate collector, thermal energy storage tank, and absorption chiller is analyzed in this work. A dimensionless model is developed from the energy balance on each component and the chiller's characteristic performance curves. The model is used to determine the interaction and influence of different parameters such as tank size, solar collector area, chiller size, cooling load, cooling temperature, heat loss, and mass flow rates on the performance. From the analysis, smaller solar collector areas are required for lower cooling loads and smaller tank volumes. A specific cooling load of 1 x 10^-5 will require a specific solar collector area between two and six times larger, depending on the initial tank temperature, than the area required for a baseline system that considers typical commercial design and operation parameters. A similar behavior was observed for the specific tank volume. For the baseline system, the minimum specific area of the collector of 9.57 is achieved for an initial tank temperature of 1.19. For a cooling load of 1 x 10^-5, the optimum initial tank temperature will be 1.11 that results in a minimum specific solar collector area of 25.26. A specific tank volume of 4 x 10^-4 will also have an optimum initial tank temperature of 1.11 that minimizes the specific solar collector area to a value of 28.18. The approach and analysis in this work can be used to determine design parameters for solar absorption cooling systems based on a proper relation among system's dimensions to achieve optimum operation.
Particle acceleration during magnetic reconnection is a long-standing topic in space, solar, and astrophysical plasmas. Recent 3D particle-in-cell simulations of magnetic reconnection show that particles can leave flux ropes due to 3D field-line chaos, allowing particles to access additional acceleration sites, gain more energy through Fermi acceleration, and develop a power-law energy distribution. This 3D effect does not exist in traditional 2D simulations, where particles are artificially confined to magnetic islands due to their restricted motions across field lines. Full 3D simulations, however, are prohibitively expensive for most studies. Here, we attempt to reproduce 3D results in 2D simulations by introducing ad hoc pitch-angle scattering to a small fraction of the particles. We show that scattered particles are able to transport out of 2D islands and achieve more efficient Fermi acceleration, leading to a significant increase of energetic particle flux. We also study how the scattering frequency influences the nonthermal particle spectra. This study helps achieve a complete picture of particle acceleration in magnetic reconnection.
The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen-based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This article introduces the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy-duty (HD) fuel cell electric commercial vehicles (FCEV). This constraint-based optimization model considers existing fueling locations, regional-specific vehicle fuel economy and weight, vehicle origin and destination (O-D), and vehicle volume by class and infrastructure costs to characterize in-mission refueling requirements for a given freight corridor. The authors applied this framework to determine the ideal public access locations for hydrogen refueling (constrained by existing fueling stations), the minimal viable cost to deploy sufficient hydrogen fuel dispensers, and associated equipment, to accommodate a growing population of hydrogen fuel cell trucks. The framework discussed in this article can be expanded and applied to a larger interstate system, expanded regional corridor, or other transportation network. This article is the third in a series of papers that defined the model development to optimize a national hydrogen refueling infrastructure ecosystem for HD commercial vehicles.
The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This paper develops a long-term plan for refueling infrastructure deployment using the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy duty (HD) fuel cell electric commercial vehicles (FCEV). This constraint-based optimization model considers existing fueling locations, regional specific vehicle fuel economy and weight, vehicle origin and destination (OD), vehicle volume by class and infrastructure costs to characterize in-mission refueling requirements for a given freight corridor. The authors applied this framework to determine the ideal long term public access locations for hydrogen refueling (constrained by existing fueling stations and dispensing technology), the minimal viable cost to deploy sufficient hydrogen fuel dispensers, and associated equipment, to accommodate a growing population of hydrogen fuel cell trucks. So the framework discussed in this paper can be expanded and applied to additional electrified powertrains as well as a larger interstate system, expanded regional corridor, or other transportation networks.