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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
NASA NTRS2018

GOES-R Lithium-Ion Battery Life Test & Workhorse Battery Performance

The GOES R series lithium-ion battery life test results are presented for the next-generation GOES R series weather satellites. Lockheed Martin is under contract to NASA Goddard to build and deliver four GOES-R series satellites which are operated by NOAA. In addition, Lockheed Martin is required to build a GOES R series flight battery to flight drawings and processes and perform an accelerated geosynchronous earth-orbiting (GEO) life test consisting of 30 real-time eclipse seasons and 30 accelerated solstice seasons. The GOES R series batteries are designed and built by Saft America, Inc. Space and Defense Division in Cockeysville, MD with Saft VL48E cells. The GOES R series life test battery which has been built to flight drawings and processes has undergone 21 seasons of accelerated GEO testing (which is equivalent to over 10 years on-orbit). This presentation includes a description of the life test and latest test results through season 20. The life test battery capacity measurements prior to cycling and after seasons 4, 10, and 20 which have shown little or no change are summarized. In addition, the performance of the GOES R series workhorse batteries performance are presented. These GOES R series workhorse batteries are used during spacecraft testing and have shown no discernable capacity loss after 7 years of mostly ambient operation and cold storage since activation. An overview of the electrical power subsystem and battery charge is, also, addressed.

Tucker, Jon↗
NASA NTRS1969

High-energy, high-power, long-life battery

High-energy-density primary battery achieves energy densities of up to 130 watt hrs./lb. The electrochemical couple consists of a lithium anode, a copper-fluoride cathode, and uses methyl formate/lithium hexafluoroarsenate for the electrolyte. Once achieved, battery life is approximately 30 hours.

Abens, S. G.↗
DOE OSTI2025

Physical Interpretation of Early Battery Life Prediction Models

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), d Q· d V −1 , and ΔV(SOC) curves. A thorough study on the dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and d Q· d V −1 models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.

25 ENERGY STORAGE↗
NASA NTRS2010

The Extravehicular Maneuvering Unit's New Long Life Battery and Lithium Ion Battery Charger

The Long Life (Lithium Ion) Battery is designed to replace the current Extravehicular Mobility Unit Silver/Zinc Increased Capacity Battery, which is used to provide power to the Primary Life Support Subsystem during Extravehicular Activities. The Charger is designed to charge, discharge, and condition the battery either in a charger-strapped configuration or in a suit-mounted configuration. This paper will provide an overview of the capabilities and systems engineering development approach for both the battery and the charger

Russell, Samuel P.↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler [National Renewable Energy Lab. (NR↗
NASA NTRS1984

Ni-cd Battery Life Expectancy in Geosynchronous Orbit

The feasibility of using nickel cadmium batteries as an alternate if flight qualified NiH2 batteries are not available is explored. Battery life expectancy data being a key element of power system design, an attempt is made to review the literature, life test data and in orbit performance data to develop an up to date estimate of life expectancy for NiCd batteries in a geosynchronous orbit.

Broderick, R. J.↗
NASA NTRS1977

Battery life test using reconditioning

A discussion is presented on nickel cadmium battery life tests using reconditioning and some comparative tests not using reconditioning. The discussion is aimed at the program application part of the testing. The goals of the program were to get an increased utilization out of the battery system in geosynchronous orbit. An attempt was made to push the depth of discharge operation up around 80 to 85 percent and the intent with the reconditioning program was to extend this type of utilization out towards a 10-year life and attune the voltage regulation.

Sparks, R. H.↗
DOE OSTI2025

Data-Driven State of Health Estimation for Second-Life Batteries Using Interpolated Synthetic Data and Feature Selection

Accurate estimation of the State of Health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

feature selection↗
DOE OSTI2026

Direct electrode-to-electrode regeneration of end-of-life batteries via electrode–electrolyte interphase dissolution

Lithium-ion battery recycling remains constrained by processes that recover metals at the expense of electrode integrity, while even direct recycling typically requires shredding to black mass followed by binder removal, separation, and full electrode refabrication. Here, we introduce direct electrode-to-electrode regeneration (DEER), a simultaneous electrochemical regeneration of used NMC and graphite electrodes from end-of-life batteries in their intact form by dissolving the passivating electrode–electrolyte interphase (EEI). DEER employs 1,3-dimethyl-2-imidazolidinone (DMI), a high donor number solvent that creates a thermodynamic environment favorable for solubilizing redox inactive EEI components. DEER dissolves the thick EEI on both used electrodes while preserving electrode integrity, enabling up to 95% capacity regain and improved cycling stability with a residual LiF-rich interphase. Operando Raman, operando IR, and post-mortem NMR directly track the electrochemically driven dissolution of carbonate-derived EEI species in the used DMI-based recycling electrolyte. Technoeconomic and life-cycle analyses show that DEER reduces the cost of recycled cell manufacturing by 56% relative to pyro- and hydrometallurgy, while lowering energy use and greenhouse gas emissions. Overall, DEER establishes the first validated pathway to directly regenerate and reuse electrodes harvested from truly end-of-life batteries, converting the key interfacial bottleneck into a controllable dissolution process and opening a practical route toward electrode level circularity.

Kim, Kiwon [Cornell Univ., Ithaca, NY (United Stat↗
DOE OSTI2025

Techno-Economic Assessments of Second-Life Batteries for Electric Vehicle Charging Stations

When electric vehicle (EV) batteries degrade below a certain capacity, they may no longer be suitable for automotive use but can be repurposed as second-life batteries (SLBs) for other applications, such as EV charging stations. When integrated with photovoltaic (PV) systems, SLB can store surplus solar energy, reducing reliance on the grid and lowering operational costs. This paper presents a novel techno-economic assessment framework for deploying SLBs in combination with PV in grid-connected EV charging stations. The proposed framework integrates the value proposition, charging station operation, optimal dispatch strategies, battery degradation modeling, input data requirements, and detailed procedures for generating key economic performance metrics. Insightful analyses are performed to assess the performance of SLBs in comparison to new batteries across various cost scenarios. The results indicate that SLBs become financially attractive when their cost is 40% or lower than new batteries.

energy storage↗
DOE OSTI2026

Strategies for Enhancing Battery Life Under Fast Charging: Insights from NMC-Based Cell Cycling

Fast charging improves the usability of consumer electronics and electric vehicles (EVs) by reducing range anxiety and downtime but accelerates battery degradation and raises safety concerns. Optimizing operational conditions during fast-charging is critical to mitigating aging and ensuring safety. This study evaluated multilayer Gr/NMC811 cells under various conditions, including depths of discharge (DODs of 68%, 84%, and 100%), upper charge cutoff voltages (4.1–4.2 V), and post-charge rest periods (2–30 min), using a 20 min fast charging protocol for up to 500 cycles (up to 150,000 miles of EV use assuming 3.3 mi/kWh vehicle level energy efficiency). Surprisingly, higher DODs under fast charging improved battery life and performance compared to lower DODs. Reducing the upper charge cut-off voltage helped mitigate degradation. A brief 2 min rest period after charging further reduced aging effects. The primary aging modes were loss of lithium inventory and cathode active material. Although minor lithium plating was observed within 500 cycles, it did not affect performance significantly. These findings suggest that, with optimized conditions, cells can sustain hundreds of fast charge cycles—equivalent to over 100,000 miles of EV use—without significant adverse effects on performance or longevity.

25 - ENERGY STORAGE↗
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 [National Renewable Energy Lab. (NREL↗
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 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

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↗