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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Systematic feature design for cycle life prediction of lithium-ion batteries during formation

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid-electrolyte interphase formation and the long testing time (∼100 days) for cells to reach the end of life. We propose a systematic feature-design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. By only using two simple Q (V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, we achieved an average of 9.87% error for cycle life prediction. Here, the physics-based investigation guided by the two designed features shows that the voltage ranges identified by our framework capture the effects of formation temperature and microscopic-particle resistance heterogeneity. By designing highly predictive, robust, and interpretable features, our approach can accelerate industrial battery formation research, leveraging the interplay between data-driven feature design and mechanistic understanding.

25 ENERGY STORAGE↗
DOE OSTI2021

Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols

Advancing lithium-ion battery technology requires the optimization of cycling protocols. A new data-driven methodology is demonstrated for rapid, accurate prediction of the cycle life obtained by new cycling protocols using a single test lasting only 3 cycles, enabling rapid exploration of cycling protocol design spaces with orders of magnitude reduction in testing time. We achieve this by combining lifetime early prediction with a hierarchical Bayesian model (HBM) to rapidly predict performance distributions without the need for extensive repetitive testing. The methodology is applied to a comprehensive dataset of lithium-iron-phosphate/graphite comprising 29 different fast-charging protocols. HBM alone provides high protocol-lifetime prediction performance, with 6.5% of overall test average percent error, after cycling only one battery to failure. Here, by combining HBM with a battery lifetime prediction model, we achieve a test error of 8.8% using a single 3-cycle test. In addition, the generalizability of the HBM approach is demonstrated for lithium-manganese-cobalt-oxide/graphite cells.

25 ENERGY STORAGE↗
DOE OSTI2022

Battery calendar aging and machine learning

Batteries continue to attract immense attention due to the critical need for new energy technologies that can help humankind transition to a net-zero future. To meet global goals for implementation of new batteries, the research community needs to more coherently consider how to go about the process of innovation, development, and validation of new technologies. Current practices for bringing a new battery technology from the benchtop to deployment can be very time consuming and complex, given that batteries can have distinct aging pathways based on both how they are cycled and their storage conditions. In nearly all use cases, batteries experience periods of active cycling and also extended periods of rest. Thus, calendar time often occurs when a battery is close to fully charged. Currently, the combined cycle and calendar life aspects receive inconsistent attention during most stages of research and development. For batteries to fulfill the critical role envisioned to meet global energy demands, greater uniformity in practice is needed to alleviate potential delays caused by the inconsistent acquisition of aging data. In this study, we present our thoughts and recommendations on the need to further advance commonality and availability of calendar aging data for emerging high-energy and long-duration batteries. While there is a broad need to adopt best practices for both calendar and cycle life assessment, a push to greater uniformity in approach and reporting is sorely needed to allow calendar life predictions to harness many of the advances that have recently been seen for cycle life prediction using machine learning (ML) and other techniques.

25 ENERGY STORAGE↗
DOE OSTI2025

Physics-Based Analysis of Cell Imbalances and Aging in Lithium-Ion Battery Modules and Packs

Lithium-ion battery (LIB) packs are a key solution for grid-scale energy storage, enabling grid resilience and supporting critical infrastructure. LIB modules and packs experience current imbalances and uneven cell aging due to various design and operational factors, and require a battery management system (BMS) to continuously monitor and control. In this context, a physics-based modeling framework for LIB modules and packs (liionpack) was enhanced to identify design and control strategies that minimize current imbalance and improve module/pack operation. Simulations of an 8-cell parallel-connected module demonstrate that reducing current imbalance leads to more uniform cell aging and improved module/pack-level degradation predictions. The analysis shows that current imbalance are affected by the electrical resistances. Terminal location significantly affects imbalance, with opposite-end terminal connections at intermediate branches minimizing the imbalance, and the pack circuit construction influences the accuracy of physics-based analysis at the pack scale. This framework enables design optimization of modules and packs through a fast and easy evaluation of pack performance and aging, and supports the development of aging-informed balancing strategies compatible with BMS implementation. Thereby, offering practical pathways to improve reliability and cycle life predictions in large-scale battery energy storage systems.

Ayalasomayajula, Surya Mitra [Oak Ridge National L↗
DOE OSTI2025

University of Miami G-band Vapor Radiometer Calibration (UMGVR_CAL) Field Campaign Report

Cold-air outbreak (CAO) clouds in the Arctic are commonly mixed-phase (MP); however, the partitioning of the amount of ice and water in CAO clouds and precipitation is not always well observed. Understanding how cloud phases partition as a function of cloud life cycle is important for predicting snowfall rates, convective life cycle, and intensity at weather timescales. The partitioning into liquid versus ice also has radiative impacts that are consequential for climate. These concerns motivated the incorporation of an airborne G-band vapor radiometer (GVR) into an National Science Foundation-supported aircraft campaign named the Cold Air outbreak Experiment in the Sub-Arctic Region (CAESAR). The GVR is an upward-pointing passive microwave radiometer using four frequencies centered around the 183.31 GHz water vapor absorption line, displaced by +- 1, 3, 7, and 14 GHz. For context, The U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility operates a surface-based GVR at its North Slope of Alaska (NSA) site. The same GVR has been used previously for a field campaign in the southeast Pacific, where an offset was noticed between brightness temperatures (Tbs) measured under clear skies compared to those calculated from a radiative transfer model. To account for any calibration offsets to the Tbs, a request was made to DOE to allow the GVR to operate at ARM’s Southern Great Plains (SGP) observatory and enable comparisons between its measurements and those available at SGP. This request was granted, titled ‘UMGVR_CAL’, short for ‘UMGVR_Calibration’. From October 30 to November 10, 2023, the GVR was deployed to the ARM SGP site to take advantage of their regular, nearby radiosonde launches under clear-sky conditions. The latter were determined using the SGP total sky imagery data. The GVR brightness temperatures in these clear-sky conditions were compared to those calculated by a radiative transfer code (PAMTRA) based on the SGP radiosondes. During the campaign, four suitable clear-sky episodes could be used for the GVR calibration. While few in number, these proved to be enough to satisfy our goal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗
DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗
DOE OSTI2025

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithiumion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery’s current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery’s degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. Here, in this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning.

battery aging reconstruction↗
DOE OSTI2021

Emergent Recursive Multiscale Interaction in Complex Systems

This project studied the potential for multiscale group dynamics in complex social systems, including emergent recursive interaction. Current social theory on group formation and interaction focuses on a single scale (individuals forming groups) and is largely qualitative in its explanation of mechanisms. We combined theory, modeling, and data analysis to find evidence that these multiscale phenomena exist, and to investigate their potential consequences and develop predictive capabilities. In this report, we discuss the results of data analysis showing that some group dynamics theory holds at multiple scales. We introduce a new theory on communicative vibration that uses social network dynamics to predict group life cycle events. We discuss a model of behavioral responses to the COVID-19 pandemic that incorporates influence and social pressures. Finally, we discuss a set of modeling techniques that can be used to simulate multiscale group phenomena.

42 ENGINEERING↗
DOE OSTI2025

HyBlend Collaborative Research Partnership (CRADA Final Report)

This agreement assembles a multi-lab, multi-industry team to address high-priority research topics related to the blending of hydrogen (H2) into the U.S. natural gas (NG) pipeline network. There are four main research objectives: 1. Compatibility of metals (SNL) – Develop general principles for operation of HyBlend™ delivery systems in the context of structural integrity and assess the role of gas impurities on degradation of metal pipelines. 2. Compatibility of polymers (PNNL) – Assess gas impurities in HyBlend for polymer pipeline degradation and lifetime predictions. 3. Life cycle analysis (LCA) (ANL) – Analyze the life cycle of technology pathways for hydrogen and NG blends, as well as alternative pathways. 4.Techno-economic analysis (TEA) (NREL) – Quantify the costs and opportunities for hydrogen production and blending with the NG network, as well as alternative pathways.

08 HYDROGEN↗
DOE OSTI2020

Evaluation of H-Canyon Ventilation Exhaust Tunnel Inspection at the Savannah River Site - 20052

The Department of Energy (DOE) H-Canyon Facility at the Savannah River Site (SRS) is operated by Savannah River Nuclear Solutions (SRNS). The Facility Structural Integrity Program [1] provides a process to inspect, evaluate, and document the conditions of passive safety related structural systems and components, their degradation mechanisms and their impact on the safety envelope. The Structural Integrity Program performs periodic visual inspections to confirm the systems and components can perform their safety function and recommend corrective actions before the function is compromised. The H-Canyon Exhaust Tunnel is part of the H-Canyon Ventilation System and is periodically inspected under the Structural Integrity Program. The Tunnel performs a Safety Class passive design feature function that is available 100% of the time. The underground reinforced concrete Tunnel directs Canyon process air from the Canyon to the Sand Filter System. The radiological and chemical airborne activity coupled with the physical configuration of the tunnel precludes a manned entry into the tunnel to perform the inspection. Unlike active systems that can be subjected to physical testing to confirm capability, qualification calculations, including modeling, is performed to assess passive design feature capabilities relative to operational, accident and natural phenomena hazards events. Input for these calculations come from knowledge of field conditions, design drawings and conservative assumptions. The knowledge of field conditions comes from concrete and soil testing activities as well as inspection data. The use of cameras on crawlers to obtain inspection data and how that data is used in qualification calculations is the focus of this discussion. Remote Tunnel inspections have been performed using cameras on a stick, referred to as pole cameras, prior to Calendar Year 2003 (CY2003). Since CY2003, six camera equipped crawlers have been used. Crawlers have evolved and improved by incorporating lessons learned from the previous inspections. The latest 2019 crawler successfully traveled the entire length of the tunnel between the Canyon and Sand Filter System and captured images that were previously unseen, thus providing key visual data for the Structural Integrity Program report [2]. The inspection data serves as 1) input to qualification calculations, 2) validates current conditions remain bounded by qualification calculations, and 3) documents a record of change over time. This data is vital for the effective evaluation of the tunnel structure and aids with predicting the life cycle service life safety envelope to protect the public, environment, and facility worker. The goal of the 2019 deployment was to use a more robust vehicle to ensure travel over obstacles, perform the inspection with higher resolution cameras, and have the ability to elevate the cameras to view surfaces previously obscured by abandon-in-place ducts in the tunnel. The inspection information will serve to strengthen the input basis for qualification calculations, versus using excessive conservative calculation assumptions. This 2019 inspection was compared with previous crawler visual evidence to identify change over time to project remaining service life and confirm field conditions remain bounded by qualification calculations. The results of the 2019 Exhaust Tunnel inspection continue to improve and provide a firm basis to make sound engineering judgements regarding tunnel capability and approaches in projecting remaining service life with an aging system and component. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Projecting Life-Cycle Environmental Impacts of Corn Production in the U.S. Midwest under Future Climate Scenarios using machine learning approach

Emission factors and process-based mechanism models are popular approaches used to estimate life-cycle environmental impacts. However, emission factors are often incapable of describing spatial and temporal heterogeneity of agricultural emissions, whereas process-based mechanism models, capable of capturing the heterogeneity, tend to be very complicated, and time-consuming. Efficient predictions of life-cycle environmental impacts from agricultural production are lacking. This study develops a rapid predictive model to quantify life-cycle global warming (GW), eutrophication (EU) and acidification (AD) impacts of corn production using a novel machine learning approach. We used the boosted regression tree (BRT) model to estimate current and future life-cycle environmental impacts of corn production in 12 U.S. Midwest at county scale under four Intergovernmental Panel on Climate Change (IPCC)’s climate scenarios (RCP2.6. 4.5, 6.0, and 8.5) for years 2009-2100. Results show that future life-cycle GW, EU and AD impacts of corn production will increase under all four scenarios, with the worst environmental impacts shown under the RCP 8.5 scenario. Significant spatial variation were estimated to be 3.6, 29.9 and 1.8-folds under the RCP4.5 scenario, and 6.6, 152.9 and 1.6-folds under the RCP8.5 scenario, respectively. Findings from this study demonstrate the importance of considering county-level life-cycle environmental impacts and the influences of climate change in future years to aid in developing potential adaptation and mitigation programs and policies.

Machine learning, climate change, projections, env↗
DOE OSTI2020

Closed-loop optimization of fast-charging protocols for batteries with machine learning

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years. Furthermore, both large parameter spaces and high sampling variability necessitate a large number of experiments. As such, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users. We combine two key elements to reduce the optimization cost: an early-prediction model, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.

25 ENERGY STORAGE↗
DOE OSTI2021

Quantifying the Influence of Charge Rate and Cathode-Particle Architectures on Degradation of Li-Ion Cells Through 3D Continuum-Level Damage Models

In this article, we develop a 3D, continuum-level damage model implemented on statistically generated LiNi 0.5 Mn 0.3 Co 0.2 O (NMC 532) secondary cathode particles. The primary motivation of the particle-level model is to inform cathode-particle design through detailed exploration of the influence of secondary and primary particle sizes on the damage predicted during operation, and determine charging profiles that reduce cathode fracture. The model considers NMC 532 secondary particles containing an agglomeration of anisotropic, randomly oriented grains. These brittle, Ni-based cathodes are prone to mechanical degradation, which reduces overall battery cycle life. The model predicts that secondary-particle fracture is primarily due to non-ideal grain interactions and high-rate charge demands. The model predicts that small secondary-particles with large grains develop significantly less damage than larger secondary particles with small grains. The model predicts most of the chemo-mechanical damage accumulates in the first few cycles. The chemo-mechanical model predicts monotonically increasing capacity fade with cycling and rate. Comparing to experimental results, the model is well suited for capturing initial capacity fade mechanisms, but additional physics is required to capture long-term capacity fade effects.

25 ENERGY STORAGE↗