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DOE OSTI2022

If One GEB is Good, a Community of GEBs is Better

Energy efficient, connected, grid-interactive, smart and flexible buildings are key to decarbonization, lowering energy use and improving the nation’s electricity grid. The U.S. Department of Energy’s Connected Communities initiative works to demonstrate how coordinated groups of highly efficient buildings combined with other distributed energy resources (DERs), such as electric vehicle (EV) charging, batteries, storage, demand response and photovoltaic (PV) generation can reliably and cost-effectively serve as grid assets by strategically deploying efficiency and demand flexibility while reducing carbon emissions. In 2021, DOE competitively awarded $61 million to a diverse portfolio of 10 pilot projects to promote grid-interactive efficient buildings (GEBs) working together to reliably and cost-effectively serve as grid assets while decarbonizing. Two of the main tenets of the program are measuring the communities’ energy and carbon performance and understanding how to replicate project successes in other communities. This paper begins with a discussion of what Connected Communities are (including a brief history) and their many benefits, including reduced carbon emissions and increased building efficiency and demand flexibility. Next, it provides an overview of the 10 projects, highlighting the diversity of approaches to measure success and replicate the projects: geographic locations; building types; utility, regulatory, market environments; and building vintages that will be used to test the ability of buildings to serve as grid resources. It concludes with a discussion of anticipated project impacts and the metrics that will be used to evaluate the Connected Communities projects.

Nemtzow, David↗

DOE OSTI2024

Calcined Polyethyleneimine Coated Optical Fibers for Distributed pH Monitoring at High Pressures and Temperatures

In the oil and gas, CO2 sequestration, H2 subsurface storage, and geothermal energy sectors, subsurface pH measurements are critical for monitoring the geochemical conditions and estimating potential corrosion rates of wellbore systems. Real-time pH measurements in these conditions are vital for detecting and predicting corrosion deterioration of wellbore components that may jeopardize the safety and continued operation of wellbore systems. Building off of earlier TiO2 based pH sensors and the known pH sensitivity of amine-based polymers, a coating based on the secondary amine polymer polyethyleneimine (PEI) was developed. The sensor coating was treated with a high temperature (500 °C) calcination procedure in air to convert it into a more stable oxidized coating capable of withstanding hot aqueous solutions without dissolving while retaining linear pH sensitivity from pH values between 2 and 11. The sensor performance was measured using optical transmission measurements in solutions of various pHs and using optical backscatter reflectometry for distributed pH sensing demonstration in wellbore-relevant pressures (up to 1000 psi) and temperatures (80 °C). A calibration curve with strong differentiation between acidic and alkaline pH was developed for both transmission-based and distributed pH measurements, using fixed wavelength transmission and integrated linear amplitude of backscattered light for distributed measurements.

Shumski, Alexander↗

DOE OSTI2024

Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

58 GEOSCIENCES↗

DOE OSTI2022

Advanced Technology and a Resilient Electric Grid

Making the electric grid cleaner and more resilient will require investing in a variety of new and emerging technologies. Advanced technologies including hydrogen, small modular nuclear reactors, and energy storage will all have a role to play in modernizing the electric grid. A diverse energy supply helps to keep the lights on and protects critical infrastructure, however it will also be important to assess the potential vulnerabilities and risks that come with unfamiliar and new technologies. This webinar will feature experts on resilience planning around advanced technologies from multiple sectors and share resources for states as they seek to modernize and decarbonize their electric grid.

advanced technology↗

DOE OSTI2020

Assessing uncertainty in the timing of energy use during cost-optimal distributed energy technology selection and sizing

This paper empirically derives uncertainty ranges in cost-optimal solar PV and storage sizing by comparing results from the REopt Lite optimization platform from metered data and a set of simulated Department of Energy Commercial Reference Building (CRB) profiles at 65 sites. We find load profile shape alone does not explain a site’s optimal configurations (i.e., PV, Storage, PV and Storage, No System). Still, load profile shape does introduce uncertainty to optimal PV and storage capacities. Across all cases where PV is part of an optimal configuration, we find the average ratio of power capacities derived from metered loads to capacities derived from CRB profiles to be 0.97 (and as high as 1463), where 1 would be a perfect match in system size. For storage, the ratio is 1.6 (and as high as 42). We also assess how, in the absence of complete metered data, a CRB profile can be selected that would be expected to yield the most similar solar PV and storage capacities. From those metrics that can be available from billing data (i.e., peak demand, monthly load totals), we find that uncertainty is most reduced by selecting the CRB’s with an annual peak occurring at the most similar time, or those with the lowest average root mean square error (RMSE) among monthly peak loads. This research can help improve the implementation and interpretation of results derived from simulated load profiles and is an important next step in advancing smart grid solutions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Acoustic Impedance Inversions for Offshore CO 2 Storage: South Georgia Embayment, United States

This study builds and expands on previous CO 2 storage resource assessment studies of the southeastern offshore Atlantic margin by providing a detailed evaluation on how rock porosities and permeabilities are distributed across the Upper Cretaceous strata restricted to the South Georgia Embayment (SGE). Using legacy industry two-dimensional seismic reflection and well data, this assessment is the first application of multiple seismic inversion techniques in this area. This workflow provides a reliable and repeatable model-based inversion which gives an improved image to discriminate lithology and predict porosity. The workflow is applicable to future CO 2 storage resource assessment studies elsewhere. The inversion results indicate that distinct porosity and permeability regimes are present and distributed in the Upper Cretaceous strata within the SGE. The impedance and porosity relationships show well-founded and reliable correlation. These relationships reveal low impedance coincident to the high porosity intervals which are proposed as potential reservoir intervals for CO 2 storage. In addition, the result shows that the Upper Cretaceous strata have two main potential reservoirs in the lower part. These are overlain by a thick impermeable interval, mostly shale, which has high impedance, low porosity, and low permeability and extends within the SGE. This result is in agreement with a previous study that also proposed two significant storage reservoirs for CO 2 in the Upper Cretaceous strata. Since porosity distribution is estimated using multiple methods, it follows the trends of seismic signature and structures of the Upper Cretaceous strata. The extracted values of porosity, ranging from 15 to 36%, and permeability, ranging from 1 to 100 mD, are close to the measured values from the well core data at the Upper Cretaceous strata interval.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Black Start with Inverter-Based Resources: Hardware Testing

Black start, or grid restoration after a wide-spread power outage, is a critical service on the power system that has historically been provided by transmission-connected synchronous generators. As the power system transitions to rely on more distributed and inverter-based resources it will be critical that these resources can also provide black start services. In this work we investigated battery energy storage and solar photovoltaics technical capabilities and limitations to provide black start services through hardware testing in an experimental microgrid testbed. This hardware demonstration of inverter-based resources providing black start functions can help inform grid operators on how to include these types of resources in their black start plans.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗

DOE OSTI2022

Protonic Ceramics for Energy Storage & Electricity Generation with Ammonia (Final Report)

The commercial product resulting from this R&D project will enable cost-effective synthesis of the Carbon Neutral Liquid Fuel (CNLF) ammonia, a common chemical, at a small scale for energy and industrial applications using only intermittent renewable energy, water, and air as feedstocks. These products are also capable of producing electrical energy from the CNLF in a fuel cell mode of operation. Thus, the eventual commercial product enables both single direction production of either CLNF or electricity, or alternatively functions as a fully reversible, self-contained energy storage device. The renewed interest in developing electrolysis systems is driven, in part, by the burgeoning renewable, solar, and wind industries and the need for an energy conversion and storage technology that can convert intermittent solar and wind energy into the production of hydrogen. Electrolysis systems integrated into both distributed and central renewable power plants would utilize solar and wind energy as their primary power source to produce renewably generated hydrogen for local energy storage or chemical feedstock purposes. Ammonia is an excellent surrogate for hydrogen transportation. NH3 presents an attractive alternative to hydrogen as a working fluid in reversible devices towards a sustainable green energy-oriented future. Ammonia can be easily liquefied at room temperature at about 8 bar or at -33°C at ambient pressure. In contrast, the liquefaction temperature of hydrogen is -253°C at ambient pressure. NH3 has a significantly higher volumetric energy density (12.7 MJ/L) than compressed hydrogen (4.5 MJ/L at ~70 MPa) or liquefied hydrogen (8.5 MJ/L). Additionally, ammonia is a widely used raw material for agriculture fertilizer and thus has well-established storage, transport, and handling processes (about 180 million tons of ammonia are produced annually). Techno-economic analysis suggests ammonia is the least expensive fuel among hydrogen, gasoline, natural gas, liquefied petroleum gas, and methanol.

08 HYDROGEN↗

DOE OSTI2024

Integrated protonic ceramic electrochemical cell for sustainable energy economy using water-energy nexus framework

Reliance on fossil fuels will continue for the next decades even though there are global pushes away from it to mitigate the overarching climate challenge, most especially by its highest consumers and availability. While there is a hastening global shift away from fossil fuel, integrating its assets into this technology helps limit the risk and future losses of stranded assets and reduce the cost of investment in the new technologies. Moreover, the generation of electricity from intermittent renewable sources like solar and wind has witnessed a significant surge in recent years, leading to a pressing demand for practical energy storage systems. Electrical energy storage is anticipated to play a pivotal role in the future global energy system, facilitating load-leveling operations to support the greater integration of renewable and distributed generation. Reversible electrochemical cells (RECs) offer a promising option for addressing the fossil fuel assets integration and energy storage challenges through the interconversion between electrical and chemical energy and concurrent utilizing carbon emission. In their electrolysis mode, the RECs convert electricity into durable, storable, and portable valuable chemical fuels such as syngas and methane. Conversely, the produced chemical fuels can be used as reactants in the fuel cell mode to generate electricity on demand with minimal (hydrocarbons) or zero when H2 or NH3 is used emissions. However, a challenging goal for this type of technology remains to achieve optimal operation and high roundtrip efficiencies, which has hindered the deployment of previous electrochemical cells. This dissertation demonstrates how reversible protonic ceramic electrochemical cells (RePCECs) can be integrated with fossil fuel power plants and renewable energy sources as a potential energy storage system. In this work, integrated RePCEC systems are designed and examined using computational modeling at scales to determine appropriate system configurations and operating conditions that achieve high roundtrip efficiencies. Cell level design of the PCEC is the first approach, several cells are assembled for the stack level model that is integrated into combined cycle powerplant and solar photovoltaic for the system level model. After critical literature review, this answered the operational and integration research questions proposed to address these challenges. The designed systems perform two functions, utilizing captured CO2 and storing renewable energy through co-electrolysis of steam and CO2. The co-electrolysis reaction involves endothermic water electrolysis and exothermic methanation reaction. To enhance high roundtrip efficiency, there is a need for thermal balance and management in the electrolysis mode. This involves operating the RePCEC stack under conditions that favor methane production to balance out heat needed by water electrolysis, it crucial for the RePCEC system operation. Methanation is enhanced by low temperatures. Leveraging on fabricated BCZYYb-electrolyte RePCEC, the cell model designed revealed that the optimum temperature for methane production is 450℃ at atmospheric pressure. Thus, to achieve optimum system performance, operating in the temperature range 450-525℃ is recommended at the given configuration, combining between the optimum temperature for methane production and temperature for the optimum stack roundtrip efficiency. Configuration with carbon capture system and purge stream is the optimum configuration from the seven conceptualized and evaluated. The modeling outcomes include a thermodynamic examination of integrated RePCEC systems, calibration of cell and stack level models, and steady-state simulation and integration into a 600MW combined cycle power plant retrofitted with two two-stage membrane-based carbon capture system and a wastewater treatment and recovery unit. At 100% powerplant loading, the stack and system roundtrip efficiencies are 72% and 51.37% respectively. Adding a purge stream for produced hydrogen at the system downstream improves the efficiencies to 74 and 55.48% respectively. At atmospheric pressure and 525℃, the system model suggests that a stack roundtrip of 82% is achievable, and overall system efficiency increases by reducing the energy consumption by the balance of plant components for steam generation and storage. Economic analysis of the process gives levelized cost of methane as $2.24/MMBtu lower than the conventional production route that range between $3.46/MMBtu and $9.85/MMBtu. The lifecycle analysis shows that the global warming potential for the production of methane and hydrogen from the RePCEC system is 3.83 kg CO2 eq which is lower than 9.35 kg CO2 eq emission during steam methane reforming for hydrogen production. This answered both the environmental and economic concerns in the raised research question. The proposed RePCEC configuration and analysis carried out in this dissertation to address the surge in renewable energy and challenges with PCEC technology hold significant potential in achieving large-scale energy storage while simultaneously reducing carbon emissions. These advancements, coupled with suitable governmental policies and incentive programs, have the potential to economically disrupt the natural gas industries by using RePCEC systems for methane production, thereby making them more favorable for eventual implementation and commercialization.

25 ENERGY STORAGE↗

DOE OSTI2026

Endogenous Interface Pricing for Consistent Transmission–Distribution Co-Optimization With Discrete Distribution Controls

This paper proposes an endogenous interface pricing model for day-ahead transmission–distribution co-optimization that co-determines the interface locational marginal price (LMP) and the transmission–distribution exchange, ensuring price–dispatch consistency while optimally scheduling discrete distribution controls. The formulation couples a DC optimal power flow (OPF) with a branch-flow AC OPF that schedules distributed energy resources (DERs), tap-changer settings, capacitor banks (CBs), and multi-period energy storage systems (ESSs) under feeder voltage and current limits, and is solved as a mixed-integer second-order cone program (MISOCP). In a T14–D33 system, coordinated device scheduling recovers about 90% of the distribution-to-transmission export achievable in a reference case that ignores distribution network (DN) limits, while satisfying a 1.05 p.u. voltage upper bound. In a T39–D34/D37/D123 system, a sequential decoupled benchmark produces interface LMP distortions up to 12.5% and a 7.28% mismatch in net export energy, whereas the proposed model removes these distortions and the associated settlement mismatches. Second-order cone (SOC) relaxation gaps remain below $10^{-3}$ in all cases.

Noh, Seung-Gil↗

DOE OSTI2024

Calcined Polyethyleneimine-Coated Optical Fibers for Distributed pH Monitoring at High Pressures and Temperatures

In the oil and gas, CO2 sequestration, H2 subsurface storage, and geothermal energy sectors, subsurface pH measurements are critical for monitoring the geochemical conditions and estimating potential corrosion rates of wellbore systems. Real-time pH measurements in these conditions are vital for detecting and predicting corrosion deterioration of wellbore components that may jeopardize the safety and continued operation of wellbore systems. Previous tests using metal oxide-based coatings (TiO2) provided strong responses at elevated temperatures and moderate pressure stability but provided poor differentiation between acidic and alkaline solutions. Building off the pH responsiveness of the TiO2 surface and known pH sensitivity of amine-based polymers, a coating based on the secondary amine polymer polyethyleneimine (PEI) was developed. As the polymer itself is highly water soluble and easily removed by aqueous solutions, the sensor coating was treated with a high temperature (500 °C) calcination procedure in air to convert it into a more stable oxidized coating capable of withstanding hot aqueous solutions without dissolving while retaining linear pH sensitivity from pH values between 2 and 11. The sensor performance was measured using optical transmission measurements in solutions of various pHs and using optical backscatter reflectometry for distributed pH sensing demonstration in wellbore-relevant pressures (up to 1,000 psi) and temperatures (80 °C).

Shumski, Alexander↗

DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗

DOE OSTI2022

SolarPlus Optimizer: Integrated Control of Solar, Batteries, and Flexible Loads for Small Commercial Buildings

Building-level microgrids may be a key strategy to unlock the combined potential of flexible loads, renewable generation, and energy storage. However, few software options exist for integrated control of building loads and other distributed energy resources at this scale. The commercial software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice. The SolarPlus Optimizer (SPO) is an open-source building-level microgrid control platform that uses Model Predictive Control to optimize both building loads and behind-the-meter energy storage to reduce energy bills and increase demand flexibility. This paper evaluates the capabilities of SPO in a small commercial building in Northern California under multiple electricity tariffs and demand response scenarios. Comparing SPO operation with an emulated battery and baseline operation employing a commercial optimization service, SPO reduced electricity bills by an estimated 7.3% in summer, 3.2% in spring, and 3.7% in winter. In a “load shape” scenario meant to counter the “duck curve”, SPO achieved 71% fewer violations from the load signal than the baseline control method. During a three hour long load shed event, SPO reduced cooling and refrigeration load by 38%. This research shows significant potential to provide load flexibility for building-level microgrids for this type of control systems. Finally, the paper discusses the future direction of research on open-source control systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Planning for Seattle at the Convergence of Resilience and Energy

Grid reliability metrics obscure important temporal, spatial, and categorical considerations for increasing energy resilience. Systemwide or feeder-level outage metrics do not identify which kinds of services are affected by outages, where, and for how long. These outage metrics indicate the impacts of outages but cannot measure the consequences to customers that could result from those outages. The consequences of power outages for surrounding community members are caused by disruptions to electricity-dependent critical services, rather than to electricity itself. Power outages can decrease a community's access to healthcare, fuel, safe indoor temperatures, and provisions like food and water. This project developed critical service access, a new consequence-focused resilience metric that quantifies the relative access to critical services provided to households by distribution infrastructure during normal conditions and major disruptions. We use a spatially granular grid analysis that facilitates targeted resilience interventions; dividing feeders into isolatable sections and combining those sections with the critical service access metric allows us to identify where energy improvements like solar-plus-storage microgrids could create the most benefits for community members by protecting access to food, fuel, health, shelter, and public safety services. We identify locations in a South Seattle study area that could be high priorities for resilience investment and summarize their potential neighborhood-scale resilience benefits. This analysis was complemented by direct feedback from study area residents collect through a survey and focus groups. Results can help utilities understand how and where long power outages can create real consequences for customers, set strategic targets based on that understanding, and measure the potential benefits of energy resilience upgrades for more informed decisions.

14 SOLAR ENERGY↗

DOE OSTI2023

Solar+Storage for Household Back-up Power: Implications of building efficiency, load flexibility, and electrification for backup during long-duration power interruptions [Slides]

The study analyzes the evolving role of solar+storage for home backup power during long-duration power interruptions. In particular, it evaluates how required storage sizing is impacted as homes become more efficient, flexible, and electrified. The study relies on NREL’s ResStock building modeling platform to create statistically representative distributions of the existing building stock in ten locations across the United States. It then shows how the amount battery storage required for backup power rises or falls as a series of building envelope efficiency, load flexibility, and electrification measures are applied across the building stock in each region. The study also includes sensitivities to show how backup power requirements are impacted by the timing and duration of power interruptions, and explores variation in backup power requirements across the building stock within each study location. The results demonstrate the value of pairing solar+storage with efficiency upgrades, smart home controls, and (in mild winter climates) efficient heat pump retrofits. That value comes in the form of reducing the amount of storage required and/or extending the range of interruption conditions over which a given system can provide backup power (i.e., more extreme weather and/or longer interruptions). Heat pumps in cold-weather climates can pose a challenge for solar+storage backup power, given the amount of storage required, though are a vast improvement over electric-resistance heating. Retaining existing fossil-based heating systems for occasional use during power interruptions, as either the primary or supplementary source of heat, can mitigate this challenge. Other forms of building electrification (e.g., cooking and water heating) generally have marginal impacts on backup battery sizing, given their relatively small energy demand.

14 SOLAR ENERGY↗

DOE OSTI2023

Hierarchical Flexibility Offering Strategy for Integrated Hybrid Resources in Real-time Energy Markets

This paper proposes a hierarchical model for determining the energy flexibility offering strategy of integrated hybrid resources (IHRs) in power distribution systems to participate in real-time energy markets. The proposed model utilizes the scalability, fast response time, and uncertainty observation of deep reinforcement learning (DRL) to overcome the scalability issue of operating numerous flexible resources and deliverability of energy flexibility to the real-time markets in the presence of the network constraints. To that end, the power distribution system is divided into multiple IHRs, where different types of flexible loads, energy storage systems, and solar plants with controllable inverters are operated through local IHR controllers, trained by deep deterministic policy gradient (DDPG) algorithm. Active power request and reactive power capacity of IHRs are then transmitted to a central flexibility controller, where a quadratic optimization model ensures the deliverability of the energy flexibility to the real-time energy market by satisfying the distribution network constraints. The proposed model is implemented on the 123-bus test power distribution system, demonstrating the capability of DRL-based hierarchical model for scalable operation of IHRs in order to offer deliverable energy flexibility to the real-time energy market.

Majidi, Majid↗

DOE OSTI2025

High spatial resolution neutron imaging of lithium-ion batteries: Correlating microstructure and lithium transport

Thick electrodes for lithium-ion batteries can increase the overall energy density, but increasing the electrode thickness introduces charge transport limitations. These limitations may be mitigated through proper electrode structuring. Here, high spatial resolution neutron imaging was used to understand the correlation between microstructure and lithium transport in lithium-ion anodes. Batteries with distinct graphite anode microstructures were produced and studied with high spatial resolution in operando neutron radiography to observe the effects of structure on transport. High spatial resolution neutron computed tomography was performed following in operando neutron radiography. X-ray computed tomography and scanning electron microscopy were used to observe the finer scale anode structure to complement neutron imaging. Solvent-free anodes containing a tightly-packed layered structure confined lithium movement close to the separator. This structure limited capacity, but supported better rate capability. Conversely, a more open pore structure in the wet cast anodes yielded higher capacity with reduced rate capability. Together, these results show that lithium distributions can be controlled by the macroscopic structure of the electrodes, the microstructural pore network, and the microscale active areas that support electrochemical reactions. Furthermore, multimodal imaging applying the complementary strengths of neutron and X-ray methods is shown as a tool for advancing battery design.

25 ENERGY STORAGE↗