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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Methods and systems for diagnosis of failure mechanisms and for prediction of lifetime of metal batteries

Methods for diagnosing failure mechanisms and for predicting lifetime of metal batteries include monitoring rest voltage and Coulombic Efficiency over relatively few cycles to provide profiles that indicate, by the trends thereof, a particular failure mechanism (e.g., electrolyte depletion, loss of metal inventory, increased cell impedance). The methods also include cycling over relatively few cycles an anode-free cell, having the same cathode and electrolyte as the metal battery, but with a current collector instead of the anode. Discharge capacity is monitored and profiled, and a discharge capacity curve is fitted to the discharge capacity profile to discern a capacity retention per cycle. The lifetime of the metal battery is determined using the capacity retention per cycle discerned from the anode-free cell. Related systems include a metal-based battery and an anode-free cell or a battery cell reconfigurable between a metal-based and an anode-free cell.

Li, Bin↗
DOE OSTI2023

Energy and Power Evolution Over the Lifetime of a Battery

Li-ion batteries currently are dominant energy storage devices for electric vehicles. Rechargeable batteries with lower cost, longer lifetime, and higher safety are desired in support of building of a green grid infrastructure. The continued investment in new battery materials, novel battery structures, advanced manufacturing processes, and accelerated testing/validation of battery performance has led to significant progress in battery development and deployment. Battery safety/reliability, which is essential to the success of a battery technology in the real world, naturally becomes the next big topic in battery research. Recently, the increasing interest in long-duration storage, fast charging, battery secondary use, and material recycling to build a circular industry and sustainable material supply chain has compelled further attention to understand the energy/power evolution and safety over the lifetime of a battery. Here, in this short Viewpoint, we discuss some high-level analyses on the energy/power evolution of rechargeable batteries over their life cycles aiming to inspire more discussion on the safety and sustainability of some representative and emerging battery technologies.

25 ENERGY STORAGE↗
DOE OSTI2021

Battery Performance, Thermal, and Life Modeling for Southern California Edison (Cooperative Research and Development Final Report)

The objective of this project is to develop battery performance models, thermal models and life models for SCE to use in simulation and analysis of grid services involving energy storage. NREL will fit the models to three separate chemistries using test data provided by SCE. The dataset for each chemistry will include electrical, thermal and aging response to different temperatures and cycling conditions, measured under a variety of cell-level and module-level experiments described in SCE test protocol documents. Once tuned to a test dataset, the battery system model software will provide predictions of battery energy and power loss, and thus cycle and calendar life, for any energy storage grid service power profile of interest to SCE. The automated battery life modeling and simulation tool can also be applied to any other grid battery systems as long as necessary input data is available to accelerate battery model development and battery lifetime analysis.

25 ENERGY STORAGE↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2022

An analytical method for identifying synergies between behind-the-meter battery and thermal energy storage

Electric utilities build generation capacity to meet the highest demand period, and they often pass on the costs associated with these peaking generators to building owners through demand charges. Building owners can minimize these demand charges by shifting energy use away from peak periods with behind-the-meter storage. This storage can include batteries, which can directly shift the metered load, or thermal energy storage, which can shift thermal-driven electric loads like air conditioning. However, there is a lack of research on how best to combine battery and thermal energy storage. In this study, we develop an analytical sizing method to calculate the potential demand reduction and annualized cost savings for different combinations of thermal and battery energy storage sizes. We show that adding batteries to a thermal energy storage system can increase the total system's load shaving potential. This is particularly true when the building has onsite photovoltaic generation or electric vehicle charging, which add significant variability to the load shape. We also show that for a given total storage size, selecting a higher fraction of thermal energy storage can significantly lower the cycling of the battery, and therefore extend the battery life. This, combined with the expected lower first cost of thermal energy storage materials compared to batteries, shows that hybrid energy storage systems can outperform a standalone battery or standalone thermal storage system. Assuming the thermal storage has a capital cost 6x lower than the battery, our analysis shows that the optimal system is 71% thermal energy storage and 29% battery energy storage for a scenario with electric vehicle charging. The annualized cost savings for this system are $48.6 k/yr, whereas an equivalently sized standalone thermal energy storage system would provide annualized cost savings of $28.5 k/yr and a standalone battery would lead to savings of $8.72 k/yr. The hybrid system also reduces battery cycling by 52% compared to a standalone battery, extending battery lifetime.

25 ENERGY STORAGE↗
DOE OSTI2023

Synergies Between Building-Sited Batteries and Thermal Energy Storage

As renewable penetration increases, there is a greater need for energy storage systems located at buildings. This storage can include batteries, which directly shift the metered load, or thermal energy storage, which shifts thermal-driven electric loads like air conditioning. This presentation covers modeling results of the potential demand reduction and annualized cost savings for different combinations of thermal and battery energy storage sizes. It also shows how battery storage can expand the usefulness of thermal energy storage for electric load shaving, and how thermal storage can lower the cost of the overall storage system and extend battery lifetime by reducing cycling.

batteries↗
DOE OSTI2025

Energy Storage Best Practices Factsheet

Brief overview of energy storage best practices presented as a factsheet for a community audience. Best practices include battery operating profiles, value stacking, and impacts on battery lifetime.

Battery Energy Storage↗
DOE OSTI2025

How to Model Batteries (with PV, Stand-Alone, or Hybrids) in SAM and PySAM

This tutorial will be a deep dive into considerations for battery modeling and demonstrating how to model them in SAM, including battery chemistry, thermal modeling, degradation/lifetime, dispatch, interconnection limits and curtailment, and their associated impacts on project profits and battery lifetime. By the end of the tutorial attendees will know how to size and model both behind-the-meter and front-of-meter battery systems, including financial analysis and pairing with other PV models (including pvlib) via PySAM.

25 ENERGY STORAGE↗
DOE OSTI2020

Time-of-Use and Demand Charge Battery Controller Using Stochastic Model Predictive Control

Stationary batteries in residential and commercial buildings are often used to smooth customer load profiles and to lower customer electricity bills. Controllers for these battery systems should account for customer energy consumption, rate structures, and high internal battery temperatures, which can lead to reduced performance over the battery lifetime. It is important to consider the uncertainty in forecasting energy consumption and temperature, especially for customers with highly variable and uncertain loads. We propose a novel battery controller using stochastic model predictive control that accounts for these uncertainties and can handle complex rate structures, including demand charges. We show that the controller performs better than standard model predictive control when there is significant uncertainty in the forecast. We also show improvements in the performance with more accurate forecasts and with a more aggressive control strategy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Time-of-Use and Demand Charge Battery Controller Using Stochastic Model Predictive Control: Preprint

Stationary batteries in residential and commercial buildings are often used to smooth customer load profiles and to lower customer electricity bills. Controllers for these battery systems should account for customer energy consumption, rate structures, and high internal battery temperatures, which can lead to reduced performance over the battery lifetime. It is important to consider the uncertainty in forecasting energy consumption and temperature, especially for customers with highly variable and uncertain loads. We propose a novel battery controller using stochastic model predictive control that accounts for these uncertainties and can handle complex rate structures, including demand charges. We show that the controller performs better than standard model predictive control when there is significant uncertainty in the forecast. We also show improvements in the performance with more accurate forecasts and with a more aggressive control strategy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Battery Control Using Stochastic Model Predictive Control

Stationary batteries in residential and commercial buildings are often used to smooth customer load profiles and to lower customer electricity bills. Controllers for these battery systems should account for customer energy consumption, rate structures, and high internal battery temperatures, which can lead to reduced performance over the battery lifetime. It is important to consider the uncertainty in forecasting energy consumption and temperature, especially for customers with highly variable and uncertain loads. We propose a novel battery controller using stochastic model predictive control that accounts for these uncertainties and can handle complex rate structures, including demand charges. We show that the controller performs better than standard model predictive control when there is significant uncertainty in the forecast. We also show improvements in the performance with more accurate forecasts and with a more aggressive control strategy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1975

Microelectronic bioinstrumentation systems

The possibility of using RF fields to power biologically implanted transmitters used in biomedical experiments was investigated. This approach would be especially useful when animal subjects are strapped in chairs or confined in cages. A telemetry system using an external source of energy has the additional advantage of not being limited in operation by battery lifetime and can therefore operate for virtually infinite lengths of time. A description of a system based on this principle is given. Progress in the development of battery-driven transmitters is also reported, including an ingestible temperature telemetry system and a resistance-to-pulse frequency convertor for implantable temperature telemetry systems.

Ko, W. H.↗
NASA NTRS1981

Battery failure model derived from flaw theory

A previously derived failure model for battery lifetime is discussed in terms of growth rate of the flaw, distribution of flaw sizes, and number of flaws. Equations are presented for determining the failure model for a nickel cadmium battery.

Schulman, I.↗
DOE OSTI2021

Identification of Life Models for Li-Ion Batteries Using Penalized Regression and Bilevel Optimization

Reduced-order physics-based life models are extremely useful for rapidly predicting battery state-of-health and for simulating battery lifetime in arbitrary aging conditions. However, identification of well-parameterized models is difficult. This is because, for maximum usefulness in predicting lifetime under a variety of conditions, aging test data exhibits many degradation mechanisms, which all need to be accurately modeled. However, because aging tests are time-consuming and expensive, especially for large-format batteries, a minimum of tests are conducted while probing many stress factors. Building a well-parameterized model is then very challenging: an under-parameterized model will neglect critical degradation modes, and an over-parameterized model will extrapolate poorly to new testing conditions. To complicate this matter, the functional form of the model for any individual degradation rate can be very difficult to identify. In this work, the statistical tools of penalized regression and bilevel optimization are used to help identify both the functional forms of and optimize the parameters of reduced-order life models, accelerating identification of robust models. Model robustness is demonstrated through traditional statistics methods of cross-validation and sensitivity analysis, uncertainty quantification through bootstrap resampling and Monte-Carlo simulation, and simulation of real-world use cases.

47 OTHER INSTRUMENTATION↗
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↗
DOE OSTI2025

Stable cycling of high-mass loaded MnO 2 electrodes for sodium-ion batteries

Achieving cost-effective, sustainable solutions for large-scale energy storage are critical for advancing the global clean energy transition. In view of the challenges posed by limited lithium reserves, low-cost sodium-ion batteries (SIBs) have emerged as a promising direction, especially for grid-level energy storage. Among the various battery electrode materials, manganese dioxide (MnO 2 ) stands out as a favorable choice for such large-scale applications due to its earth abundance, cost-effectiveness, and non-toxic nature. Although MnO 2 is known as a pseudocapacitive material with superior cycling stability in aqueous electrolytes, its dissolution in non-aqueous electrolytes has restricted its use in long-lifetime batteries. In this study, we address two issues which have limited the use of MnO 2 electrodes in non-aqueous electrolytes. First, using electrochemical quartz crystal microbalance measurements in combination with other electrochemical methods, we demonstrate that diglyme (bis(2-methoxyethyl) ether) electrolyte can achieve stable cycling of electrodeposited ε-MnO 2 . These results enable us to tackle a second objective, that is increasing the mass loading of the MnO 2 electrode, since achieving high areal energy density is a significant factor in reducing manufacturing costs. Using 3D printed graphene aerogel (GA) as a scaffold, our studies show that the electrodeposited MnO 2 /GA electrodes possess scalable properties with mass loadings from 20 to 80 mg cm −2 . The resulting electrodes exhibit areal energy densities as high as 4.4 mA h cm −2 at a current density of 10 mA cm −2 . The high mass loaded MnO 2 electrodes were incorporated as a cathode in a SIB which used TiO 2 as the anode. The SIB device exhibited excellent performance with power densities in excess of 70 mW cm −2 . These studies highlight the promise of MnO 2 electrodes for use in a low-cost technology for large-scale energy storage.

25 ENERGY STORAGE↗
DOE OSTI2026

Are Capacity and Energy Loss Equivalent Metrics for Battery Aging Reporting?

Battery aging in research publications and manufacturer specification sheets for individual cells is commonly reported as capacity (Ah) versus cycle number. However, the key measured quantity in battery-powered devices is energy (Wh), which is derived from integrating capacity with voltage. In this work, we compare the rate of capacity and energy loss across a wide range of Li-ion single-cell cycling studies with different positive electrode chemistries, charge–discharge rates, and temperatures. We find that the relative rate of discharge energy loss varies with cycling conditions. For many cells cycled under moderate conditions, the rate of discharge energy fade is only slightly faster than the rate of discharge capacity fade. However, some cells demonstrated up to a 15% decline in cycle count when 80% energy retention rather than 80% capacity retention was used as the end-of-life metric. These results highlight the importance of reporting cell aging based on energy fade to avoid overestimating battery lifetime in full systems.

batteries↗