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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning

Real-time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery-powered devices. In this study, a methodology to predict the entire constant-current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO 2 -based batteries at a constant C-rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO 2 -based) using open-access datasets. The prediction error of the charge curves for the LiCoO 2 -based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.

25 ENERGY STORAGE↗
DOE OSTI2023

Size and Charge Effects on Crossover of Flow Battery Reactants Evaluated by Quinone Permeabilities Through Nafion

Organic reactants are promising candidates for long-lifetime redox flow batteries, and synthetic chemistry unlocks a wide design space for new molecules. Minimizing crossover of these molecules through ion exchange membranes is one important design consideration, but the ways in which the crossover rate depends on the structure of the crossing species remain unclear. Here, we contribute a systematic evaluation of size- and charge-based effects on dilute-solution small molecule permeability through the Nafion NR212 cation exchange membrane. We found that increasing the magnitude of charge number z with the same sign as membrane fixed charges, achieved here by successive sulfonation of quinone redox cores, results in more than an order of magnitude permeability reduction per sulfonate. Size-based effects, understood by comparing the Stokes radii of the quinones studied, also reduces permeability with increasing effective molecule size, but doubling the effective size of the redox reactants resulted in a permeability decrease of less than a factor of three.

25 ENERGY STORAGE↗
DOE OSTI2024

Li-Ion Battery Thermal Characterization for Thermal Management Design

Battery design efforts often prioritize enhancing the energy density of the active materials and their utilization. However, optimizing thermal management systems at both the cell and pack levels is also key to achieving mission-relevant battery design. Battery thermal management systems, responsible for managing the thermal profile of battery cells, are crucial for balancing the trade-offs between battery performance and lifetime. Designing such systems requires accounting for the multitude of heat sources within battery cells and packs. This paper provides a summary of heat generation characterizations observed in several commercial Li-ion battery cells using isothermal battery calorimetry. The primary focus is on assessing the impact of temperatures, C-rates, and formation cycles. Moreover, a module-level characterization demonstrated the significant additional heat generated by module interconnects. Characterizing heat signatures at each level helps inform manufacturing at the design, production, and characterization phases that might otherwise go unaccounted for at the full pack level. Further testing of a 5 kWh battery pack revealed that a considerable temperature non-uniformity may arise due to inefficient cooling arrangements. To mitigate this type of challenge, a combined thermal characterization and multi-domain modeling approach is proposed, offering a solution without the need for constructing a costly module prototype.

25 ENERGY STORAGE↗
DOE OSTI2020

Near Neutral pH Redox Flow Battery with Low Permeability and Long-Lifetime Phosphonated Viologen Active Species

A highly stable phosphonate-functionalized viologen is introduced in this work as the redox-active material in a negative potential electrolyte for aqueous redox flow batteries (ARFBs) operating at nearly neutral pH. The solubility is 1.23 m and the reduction potential is the lowest of any substituted viologen utilized in a flow battery, reaching –0.462 V versus SHE at pH = 9. The negative charges in both the oxidized and the reduced states of 1,1'-bis(3-phosphonopropyl)-[4,4'-bipyridine]-1,1'-diium dibromide ( BPP–Vi ) effect low permeability in cation exchange membranes and suppress a bimolecular mechanism of viologen decomposition. A flow battery pairing BPP–Vi with a ferrocyanide-based positive potential electrolyte across an inexpensive, non-fluorinated cation exchange membrane at pH = 9 exhibits an open-circuit voltage of 0.9 V and a capacity fade rate of 0.016% per day or 0.00069% per cycle. Overcharging leads to viologen decomposition, causing irreversible capacity fade. Our research introduces extremely stable, extremely low-permeating and low reduction potential redox active materials into near neutral ARFBs.

25 ENERGY STORAGE↗
DOE OSTI2022

Overcoming the Intrinsic Limitations of Fast Charging Lithium‐Ion Batteries Using Integrated Acoustic Streaming

A lithium‐ion battery's maximum charge rate and energy density are intrinsically limited by the ion diffusion rate in the electrolyte. Most research focuses on materials science solutions to this problem, with gradual improvement over the years. A mechanical solution is proposed to integrate an MHz‐order frequency surface acoustic wave (SAW) device into an existing 1.8 Ah multilayered Li‐ion pouch cell to enhance the ion diffusion rate and the overall battery performance. Both the charging rate and cycling lifetime are improved from SAW. At a 6C (10 min) charge and C/3 discharge rate, typical of electric vehicle applications, integrating SAW into the Li‐ion cell doubles the energy density and maintains at least 72% of the battery's initial capacity after 2000 cycles. Moreover, using SAW quantifiably reduces battery degradation in these conditions as determined by optical imaging, scanning electron microscopy, X‐ray diffraction, and neutron diffraction. The use of SAW appears to offer a method to avoid undesirable Li metal plating on the graphite anode during charging, and leads to a much longer battery lifetime and good charge capacity, all despite rapid charging.

Huang, An↗
DOE OSTI2021

Pathways towards managing cost and degradation risk of fast charging cells with electrical and thermal controls

The charging rate of Li-ion batteries is limited by the risks of lithium plating and thermal damage, both of which negatively affect lifetime of the batteries. This work analyzes how to minimize charge time and manage the risk of degradation using both electrical and thermal controls. To accomplish this, an electrochemical-thermal model is developed to introduce a constant-risk fast charging protocol which maximizes the charging current while maintaining the cell within pre-defined design limits selected to minimize the risk of degradation. Results indicate 80%-charging of a typical NMC/graphite cell can be achieved within 10 minutes with a cell cost under $100 kW –1 h –1 (corresponding to an 80 μm thick anode) when charged at moderate allowable risks of degradation. Here, we also demonstrate how stretching the allowable risk (i.e., higher temperatures, larger C-rates, and lower anode potential limits) allows affordable design of fast charging cells.

25 ENERGY STORAGE↗
DOE OSTI2024

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

25 ENERGY STORAGE↗
DOE OSTI2023

Experimental Aging and Lifetime Prediction in Grid Applications for Large-Format Commercial Li-Ion Batteries

Due to the growth of electric vehicle and stationary energy storage markets, the production and use of lithium-ion batteries has grown exponentially in recent years. For many of these applications, large-format lithium-ion batteries are being utilized, as large cells have less inactive material relative to their energy capacity and require fewer electrical connections to assemble into packs. And especially for stationary energy storage systems, where energy delivered is the only revenue source, the economics of these battery systems is highly dependent on cell lifetime. However, testing of large-format lithium-ion batteries is time consuming and requires high current channels and large testing chambers, making information on the performance of commercial, large-format lithium-ion batteries hard to come by. Here, accelerated aging test data from four commercial large-format lithium-ion batteries is reported. These batteries span both NMC-Gr and LFP-Gr cell chemistries, pouch and prismatic formats, and a range of cell designs with varying power capabilities. Accelerated aging test results are analyzed to examine both cell performance, in terms of efficiency and thermal response under load, as well as cell lifetime. Cell thermal response is characterized by measuring temperature during cycle aging, which is used to calculated a normalized thermal resistance value that may help estimate both cell cooling needs or to help extrapolate aging test results to different thermal environments. Cell lifetime is evaluated qualitatively, considering simply the average calendar and cycle life across a range of conditions, as well as quantitatively, using statistical modeling and machine-learning methods to identify predictive aging models from the accelerated aging data. These predictive aging models are then used to investigate cell sensitivities to stressors, such as cycling temperature, voltage window, and C-rate, as well as to predict cell lifetime in various stationary storage applications. Results from this work show that cell lifetime and sensitivity to aging conditions varies substantially across commercial cells, necessitating testing for specific cell formats to make quantitative lifetime predictions. That being said, all commercial cells tested here are predicted to reach at least 10-year lifetimes for stationary storage applications. Based on the aging test results and modeling, some cells are expected to be relatively insensitive to temperature and use-case, making them suited for simple use cases with little or no thermal management and simple controls, while the lifetime of other cells could be extended to 20+ years if operated with thermal management and degradation-aware controls.

battery↗
DOE OSTI2024

Battery State of Health Estimator: Cooperative Research and Development Final Report

NREL has developed a software tool to enable Renewance to estimate the degradation of batteries from basic information such as the type of battery and the application of that battery during its first life, so that used batteries may be evaluated for potential repurposing at low cost. This software tool utilizes NREL's BLAST-Lite battery degradation modeling code, which was updated with additional models for commercially produced lithium-ion batteries as a part of this CRADA. The software tool enables users to input details such as battery type and application so that lifetime estimates can be made without any programming or expert battery knowledge. The application input loads in saved values for parameters such as cycles per year, depth-of-discharge, and other battery operating parameters from a file defined by Renewance. These parameters may be modified to refine simulations for specific batteries. The software tool also incorporates a degradation model optimization tool, whereby existing battery degradation models may be tuned according to measured battery health. This ensures that new models still predict degradation behaviors expected from a certain battery chemistry, but with the overall degradation rate tuned to a specific battery make and model. The new model can then be saved for estimating the degradation of other similar batteries. An additional task was planned to utilize machine-learning to enable battery health diagnosis from rapid EIS measurements to accelerate the screening of used batteries. This task was not completed due to lack of available data for training a machine-learning model. CRADA benefit to DOE, Participant, and US Taxpayer: Further development of open-source software tool BLAST-Lite for predicting the lifetime of commercially produced Lithium-ion batteries (NREL SWR-22-69).

25 ENERGY STORAGE↗
DOE OSTI2022

Thermal Energy Grid Storage (TEGS) Using Multi-Junction Photovoltaics (MPV) (Final Technical Report)

The project aimed to develop a thermal energy storage battery that converts electricity to heat and stores heat at ultra-high temperatures (>2000°C) in graphite blocks. The thermal battery discharge uses TPV cells that directly convert thermal energy to electrical energy without any moving parts. All components of this technology were successfully demonstrated at the laboratory scale in this project. Development of extremely low cost (< $20/kWh) grid level energy storage is a crucial necessity to reach high penetrations of renewables. The thermal battery technology developed in this project is expected to meet the cost targets that would enable full renewable penetration. The project focused on four key aspects of this technology: Converting electricity to ultra-high temperature heat: This was done through development of graphite Joule heaters. Major issues related to arcing, heater evaporation and deterioration due to long term oxidation were addressed to ensure lifetime exceeding the service lifetime of the battery. Converting ultra-high temperature heat back to electricity: This was done through development of beyond state-of-the-art TPV cells. We demonstrated energy conversion efficiency of >40% that is a world record and exceeds the average energy conversion efficiency of turbines in the USA. The findings are peer-reviewed and published in Nature, and received a wide media attention globally. Protecting the TPV cells to ensure lifetime: Deposition of volatilized material, such as sublimated material or particles, on the TPV cell could greatly reduce the efficiency and lifetime of the TPV cells by blocking their view to the heat source and causing cell overheating. In this project we developed and demonstrated an approach that reduces the deposition rate, ensuring long > 30 year life. Technoeconomic feasibility and commercialization: In collaboration with the project’s technical advisory board, we developed a technoeconomic model. The model shows that, at large scales (> 1GWh) the thermal battery technology is projected to reach a cost of energy stored below $10/kWh-e, with a cost per unit power < $0.5/W-e and a roundtrip efficiency of 50%. These results, along with the technical achievements in the project led to the creation of a startup company (i.e., Fourth Power) that is pursuing commercialization of the technology.

25 ENERGY STORAGE↗
DOE OSTI2025

The Solid Electrolyte Interphase Dispersion Can Predict Cycle and Calendar Lifetimes in Silicon Anodes for Lithium-Ion Batteries

The solid electrolyte interphase (SEI) plays a critical role in lithium-ion battery (LIB) anodes. It is responsible for passivating the reactive surface of lithiated anodes against degradation of the electrolyte which enables long cycle and calendar lifetimes for LIBs. This role is especially important in high energy density anodes like silicon, that undergo massive volumetric changes during electrochemical cycling. The mechanism by which the SEI performs this role, however, is not clear which makes designing an SEI to passivate silicon anodes impossible. Through decades of research, dozens of chemical species have been identified within the SEI ranging from inorganic solids to polymeric coatings all of which simultaneously exist in a 'mosaic' composition at the anode surface. This mosaic creates a highly dispersive environment in which electrostatic screening of the anode surface from the electrolyte is not always complete. Here, we present an electrochemical technique to directly test the dispersion at the anode surface. We use this technique to screen more than 20 different electrolytes against silicon anodes and find correlations between both the cycle life and calendar life. The insights from this study offer a new framework to think about the SEI and a rapid screening method to test novel electrode/electrolyte combinations.

battery↗
DOE OSTI2022

In-situ visualization of the transition metal dissolution in layered cathodes

Transition metal dissolution in layered cathodes is one of the most intractable issues that deteriorates the battery performance and lifetime. It not only aggravates the structure degradation in cathode, but also damages the solid electrolyte interphase in anode and even induce the formation of lithium dendrites. In this work, we investigate the dissolution behaviors of polycrystalline and single-crystalline layered cathode via operando X-ray imaging techniques. The described cathode particle morphology appears to have a significant impact on the evolution of the dissolution dynamics. As a mitigation strategy, we reveal that doping with trace amount of Zr in the layered cathode could improve its robustness against the transition metal dissolution. Our finding provides valuable insights for designing the next-generation highly-stable layered battery cathodes.

25 ENERGY STORAGE↗
DOE OSTI2021

ENPOLITE: Comparing Lithium-Ion Cells across Energy, Power, Lifetime, and Temperature

Due to their impressive energy density, power density, lifetime, and cost, lithium-ion batteries have become the most important electrochemical storage system, with applications including consumer electronics, electric vehicles, and stationary energy storage. However, each application has unique, often conflicting product specifications, requiring a balanced overall assessment. The Ragone plot is a commonly-used plot to compare energy and power of lithium-ion battery chemistries. Important parameters including cost, lifetime, and temperature sensitivity are not considered. Overall, a standardized and balanced reporting and visualization of specifications would greatly help an informed cell selection process.

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 OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗
DOE OSTI2021

Atomic layer deposition of sodium fluoride thin films

The need for advanced energy conversion and storage devices remains a critical challenge amid the growing worldwide demand for renewable energy. Metal fluoride thin films are of great interest for applications in lithium-ion and emerging rechargeable battery technologies, particularly for enhancing the stability of the electrode-electrolyte interface and thereby extending battery cyclability and lifetime. Reported within, sodium fluoride (NaF) thin films were synthesized via atomic layer deposition. NaF growth experiments were carried out at reactor temperatures between 175 and 250 °C using sodium tert-butoxide and HF-pyridine solution. Here, the optimal deposition temperature range was 175–200 °C, and the resulting NaF films exhibited low roughness (R q ≈ 1.6 nm for films of ~8.5 nm), nearly stoichiometric composition (Na:F = 1:1.05) and a growth per cycle value of 0.85 Å/cycle on SiO 2 substrates. These results are encouraging for future applications of NaF thin films in the development of improved energy capture and storage technologies.

25 ENERGY STORAGE↗
DOE OSTI2023

Scale-up Production of Graphene Monoxide for Next-Generation LIB Anodes (AMMTO Final Report)

COnovate, LLC has developed a patented material called graphene monoxide and demonstrated that its unique chemical and crystal structure enables lithium-ion batteries (LIB) with greater specific capacity, safer and faster charging, and better low temperature performance than the industry standard. The current capability gap with COnovate’s technology is to scale up this new active anode material, developing processing technology to commercially relevant levels. This project will develop the means for progressive scale-up to 100g, 1kg, and 10kg levels, incorporating industrial synthesis and processing technologies, and deliver an industrial procedure/batch record draft for use by potential full-scale manufacturers to develop metric-ton production levels. The project goals of 10kg production and industrial procedure for full-scale production are necessary for COnovate’s customers to adequately evaluate and adopt their technology, and to pursue purchase orders for tons of material. MERF will confirm material analytics and performance in half cell tests during scaling and deliver materials to COnovate for QA/QC testing during the iterative scale up efforts. The final product batches will be delivered to COnovate for battery assembly and lifetime testing.

25 ENERGY STORAGE↗