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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul↗
NASA NTRS1985

AUSSAT battery life test program

AUSSAT Pty. Ltd., the Australian National Satellite organization, has contracted with the Hughes Aircraft Company (HAC) for the construction of 3 satellites based on the now familiar HS-376 product line. As part of the AUSSAT contract, HAC is conducting an extensive NiCd battery life test program. The life test program, objectives and test results to date are described. Particular emphasis is given to the evaluation of the FS2117 separator as a future replacement for the Pellon 2505 separator of which only a very limited quantity remains.

Gorian, P. W.↗
DOE OSTI2021

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.

25 ENERGY STORAGE↗
DOE OSTI2024

A Review on Solid State Batteries: Life Cycle Perspectives

This report briefly reviews the characteristics of solid-state batteries (SSBs) and the life-cycle analysis (LCA) studies that have been completed for SSBs. Compared with conventional lithium-ion batteries (LIBs), SSBs offer improved safety and potentially higher energy density — both enabled by replacing liquid electrolytes with solid electrolytes and using lithium metal anodes. Several challenges impede the commercialization of SSBs, primarily related to the stability of the interface between the solid electrolyte and the electrodes. Several options are under consideration for SSB electrolyte and cathode chemistries. As a result, the production processes for these components and the corresponding battery packs are still under development and can differ significantly from those used for conventional LIB pack production. A robust comparison of SSBs with LIBs through LCA is important to analyze the environmental benefits and challenges associated with this alternative battery system. While the literature provides only a few LCA studies focused on SSBs, with significant uncertainty in their life-cycle inventories (LCIs), those studies collectively suggest that solid electrolyte manufacturing is the major environmental hotspot, followed by cathode and anode production.

25 ENERGY STORAGE↗
DOE OSTI2026

Battery Life Prediction Using Reduced-Order Physics Models and Machine Learning (CRADA Final Report)

Phase 1 (Original CRADA, plus no-cost extension modifications #1-3, 6/1/2017 to 3/13/2021): The Australian Department of Defence (AUDoD) is performing accelerated aging tests of Li-ion batteries to benchmark their reliability and degradation characteristics. Using its previously developed battery lifetime predictive model framework, the National Laboratory of the Rockies (NLR) will develop analytical models based the AUDoD data to predict lifetime of the multiple Li-ion battery chemistries under real-world use scenarios of interest to AUDoD. The NLR model is based on physical degradation mechanisms encountered by Li-ion batteries and has been previously validated. Phase 2 (CRADA modification #4, plus no-cost extension modification #5, 2/22/2021 to 3/30/2025): Train and support Australian Department of Defence personnel to use NLR software for model-based estimation of Li-ion battery lifetime using accelerated battery aging data collected by the Australian Department of Defence. Under separate DOE funding from 2019 to 2021, NLR enhanced its battery life-prediction software using machine learning algorithms to automate portions of the model-fitting process, requiring significantly less labor and expert judgment and also adding uncertainty quantification, increasing statistical rigor. Under Phase 2, NLR will customize NLR Software and provide it to AuDoD. NLR will enhance its NLR Model to capture aging modes of AuDoD's multi-cell modules, including cell-balancing effects. NLR will develop example single-cell and multi-cell models based on one AuDoD battery aging dataset. NLR will train AuDoD personnel on NLR Software. By the conclusion of the project, NLR will have provided AuDoD the training materials, a user manual and software needed to perform their own analysis of additional and/or future battery aging datasets.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Electric Vehicle Lithium-Ion Battery Life Cycle Management

There is no question that electric vehicles (EVs), which are key for addressing climate change impacts from the transportation sector, are quickly gaining popularity and availability. With limits and other challenges related to the supply of critical battery minerals, maximizing the use of EV batteries and ensuring recovery of battery minerals is imperative. Therefore, proper end-of-life-cycle management (reuse and recycling) of these batteries must be part of the EV ecosystem from the perspective of both the supply chain and environmental footprint. Second use of batteries for energy storage systems extends the initial life of these resources and provides a buffer until economical material recovery facilities are in place. Although there are multiple pathways to recycling and recovery of materials, new recovery technologies are moving toward commercially available hydrometallurgy and promising direct recycling, which analysis has shown has the lowest overall carbon footprint. Strategically locating these plants close to battery collection further reduces transportation and thus recycling and recovering costs. Proper life cycle management could alleviate future lithium-ion battery materials supply chains for EVs. Governments and other stakeholders around the world have started initiatives and proposed regulations to address the challenges associated with life cycle management of EV lithium batteries. Finally, as manufacturers are increasingly faced with the likelihood of such extensive regulatory requirements, attention should be given to new designs, sales, and service models that can reduce life cycle management costs.

25 ENERGY STORAGE↗
NASA NTRS1971

Accelerated battery-life testing - A concept

Test program, employing empirical, statistical and physical methods, determines service life and failure probabilities of electrochemical cells and batteries, and is applicable to testing mechanical, electrical, and chemical devices. Data obtained aids long-term performance prediction of battery or cell.

Mccallum, J.↗
DOE OSTI2024

WIP: Collaborating to Introduce Second-Life Battery Technology Research in an Informal STEM Learning Environment

This work in progress, innovative practice, paper outlines an interdisciplinary collaborative model utilized by a team of educational researchers, and engineers to design a series of community-centered, place-based learning activities suitable for a middle or high school informal STEM learning environment. Based on the potential applications and benefits of Second-Life Electric Vehicle Batteries, the activities connect cutting-edge STEM research with multidimensional real-world challenges within their communities related to climate change, renewable energy integration and storage, intelligent transportation systems, and optimized energy management, that empowers students to think critically about the role they can play in shaping a more resilient, environmentally sustainable community.

Whiteside, Hope↗
NASA NTRS1984

Prediction of Battery Life and Behavior from Analysis of Voltage Data

A method for simulating charge and discharge characteristics of secondary batteries is discussed. The analysis utilizes a nonlinear regression technique where empirical data is computer fitted with a five coefficient nonlinear equation. The equations for charge and discharge voltage are identical except for a change of sign before the second and third terms.

Mcdermott, P. P.↗
DOE OSTI2023

Composite Lithium Metal Structure to Mitigate Pulverization and Enable Long‐Life Batteries

In lithium metal batteries, non‐uniform stripping of lithium results in pit formation, which promotes subsequent non‐uniform, dendritic deposition. This viscous cycle leads to pulverization of lithium which promotes cell shorting or capacity degradation, symptoms further exaggerated by high electrode areal loading and lean electrolytes. Here, to address this challenge, a composite lithium metal anode is engineered that contains uniformly distributed, nanometer‐sized carbon particles. This composite lithium is shown to strip more uniformly since the growth of non‐uniform pits is intercepted by the carbon particles. This mechanism is corroborated by a continuum electrochemical model. Subsequent lithium deposition on carbon particles is also found to be more uniform than on the surface with irregular pits. Notably, the pulverization rate of composite lithium is 26 times slower than that of commercial lithium. Moreover, in a Li‐S battery with sulfurized polyacrylonitrile cathode, the use of the composite anode extends the cycle life by three times when the areal capacity is 8 mAh cm −2 . The approach of using an engineered lithium composite structure to address challenges during both stripping and plating can inform future designs of lithium metal anodes for high areal capacity operations.

high areal capacity↗
DOE OSTI2026

Fluorinated Glyme Solvents to Extend Lithium-Sulfur Battery Life (Final Technical Report, Unlimited)

This project investigated a number of partially fluorinated glymes (PFGs) as electrolyte cosolvents to improve the performance of lithium-sulfur (Li-S) batteries. A major issue in Li-S cells is the electrochemical reaction of sulfur in the cathode to form lithium polysulfides (LPS) that dissolve in the electrolyte. Those LPS are electrochemically and chemically reactive at the lithium anode, resulting in lithium sulfide deposition on the anode and also electrochemical reaction at both the anode and cathode, leading to a “polysulfide shuttle” and reduced coulombic efficiency (CE) and self-discharge of the cell. PFGs reduce the solubility of LPS while maintaining good solubility of lithium salts such as LiTFSI. By adjusting the amount of PFG as cosolvent in the electrolyte, we showed that the solubility of LPS in the electrolyte can be tuned. (It is not desirable to completely eliminate LPS in the electrolyte, as they facilitate electrochemical reaction of the electrically insulating S 8 and Li 2 S within the cathode by shuttling charge between them and the conductive carbon.) Another issue in Li-S cells is degradation of the Li anode over many cycles of stripping (discharge) and plating (charge). We showed that PFGs have a beneficial effect on the physical morphology of the Li anode, SEI formation, and the CE of a Li-Li cell. Among the many PFGs tested, we found the best performance from PFGs designated PFG2 and PFG5, and these two PFGs were thoroughly studied. A systematic coin-cell study of electrolyte solvents of 90:10, 80:20, or 70:30 DME:PFG (DME = 1,2-dimethoxyethane) revealed some systematic trends: a higher percentage of PFG solvent led to substantially longer cycle life, but at the same time reduced specific capacity (mAh/g(S)) and cell capacity at higher rates. These studies used LiFSI as the electrolyte salt, as it was found to extend cycle life compared to LiTFSI. Finally, the addition of a small amount of 1,3-dioxolane (DOL) to the electrolyte was found to be beneficial. The overall optimal electrolyte solution was found to be 0.6 M LiFSI + 0.5 M LiNO 3 in 75:5:20 DME/DOL/PFG (either PFG2 or PFG5).

25 ENERGY STORAGE↗
DOE OSTI2021

Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.

25 ENERGY STORAGE↗
NASA NTRS1983

Modeling battery life through changes in voltage fit coefficients

A number of 12 ampere-hour cells, nickel cadmium, were cycled under various conditions of temperature and depth of discharge. Using this data, it was confirmed that a five parameter fit equation could be used to model the data within a few millivolts. Both charge and discharge curves can be fit with accuracies in the range of one or two millivolts. The fit coefficients when plotted versus cycles show definite trends and patterns which can be used in an operational sense to predict battery voltage as a function of temperature and depth of discharge.

Fox, D.↗
DOE OSTI2024

Manipulating the diffusion energy barrier at the lithium metal electrolyte interface for dendrite-free long-life batteries

Abstract Constructing an artificial solid electrolyte interphase (SEI) on lithium metal electrodes is a promising approach to address the rampant growth of dangerous lithium morphologies (dendritic and dead Li 0 ) and low Coulombic efficiency that plague development of lithium metal batteries, but how Li + transport behavior in the SEI is coupled with mechanical properties remains unknown. We demonstrate here a facile and scalable solution-processed approach to form a Li 3 N-rich SEI with a phase-pure crystalline structure that minimizes the diffusion energy barrier of Li + across the SEI. Compared with a polycrystalline Li 3 N SEI obtained from conventional practice, the phase-pure/single crystalline Li 3 N-rich SEI constitutes an interphase of high mechanical strength and low Li + diffusion barrier. We elucidate the correlation among Li + transference number, diffusion behavior, concentration gradient, and the stability of the lithium metal electrode by integrating phase field simulations with experiments. We demonstrate improved reversibility and charge/discharge cycling behaviors for both symmetric cells and full lithium-metal batteries constructed with this Li 3 N-rich SEI. These studies may cast new insight into the design and engineering of an ideal artificial SEI for stable and high-performance lithium metal batteries.

25 ENERGY STORAGE↗