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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 163 records · Page 9

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DOE OSTI2021

A machine learning degradation model for electrochemical capacitors operated at high temperature

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

42 ENGINEERING↗

DOE OSTI2024

Transition Metal Blocking Microporous Polymer Separators for Energy-Dense and Long-Lived Li-ion Batteries

A revolution in electric-powered mobility is imminent, catalyzed by breakthroughs in Li-ion battery technology that have dramatically lowered the $/kWh price tag of electrochemical energy storage. Increases in production have also enabled greater penetration in key markets. World-wide registration of new plug-in hybrid electric and battery-only electric vehicles (PHEV and BEV) increased by 70% from 2014 to 2015, and sales of PHEVs and BEVs are predicted to be 35% of all vehicle sales by 2040, reaching 41 million units per year—a 90-fold increase from 2015.1,2 In stride with industry giants such as Tesla, EV manufacturers rely on Li-ion cell Original Equipment Manufacturers (OEMs) for relentless improvements in Li-ion battery technology necessary to drive this bullish market penetration outlook. Advancing energy and power density is critical to alleviating EV-customer range anxiety. Furthermore, strategies to extend battery-pack life beyond 1000 cycles—particularly when operating under aggressive duty cycles—requires fresh perspectives and innovation in cell components.

36 MATERIALS SCIENCE↗

NASA NTRS2008

Progress of Ongoing NASA Lithium-Ion Cell Verification Testing for Aerospace Applications

A Lithium-ion Verification and Validation Program with the purpose to assess the capabilities of current aerospace lithium-ion (Li-ion) battery cells to perform in a low-earth-orbit (LEO) regime was initiated in 2002. This program involves extensive characterization and LEO life testing at ten different combinations of depth-of-discharge, temperature, and end-of-charge voltage. The test conditions selected for the life tests are defined as part of a statistically designed test matrix developed to determine the effects of operating conditions on performance and life of Li-ion cells. Results will be used to model and predict cell performance and degradation as a function of test operating conditions. Testing is being performed at the Naval Surface Warfare Center/Crane Division in Crane, Indiana. Testing was initiated in September 2004 with 40 Ah cells from Saft and 30 Ah cells from Lithion. The test program has been expanded with the addition of modules composed of 18650 cells from ABSL Power Solutions in April 2006 and the addition of 50 Ah cells from Mine Safety Appliances Co. (MSA) in June 2006. Preliminary results showing the average voltage and average available discharge capacity for the Saft and Lithion packs at the test conditions versus cycles are presented.

McKissock, Barbara I.↗

NASA NTRS1997

NiH2 Battery Reconditioning for LEO Applications

This paper summarizes reasons for and benefits of reconditioning nickel-hydrogen (NiH2) batteries used for Low Earth Orbit (LEO) applications. NiH2 battery cells do not have the classic discharge voltage problems more commonly associated with nickel-cadmium (NiCd) cells. This is due, in part, to use of hydrogen electrodes in place of cadmium electrodes. The nickel electrode, however, does have a similar discharge voltage signature for both cell designs. This can have an impact on LEO applications where peak loads at higher relative depths of discharge can impact operations. Periodic reconditioning provides information which can be used for analyzing long term performance trends to predict usable capacity to a specified voltage level. The reconditioning process described herein involves discharging NiH2 batteries at C/20 rates or less, to an average cell voltage of 1.0 volts or less. Recharge is performed at nominal C/5 rates to specified voltage/temperature (V/T) charge levels selected to restore required capacity with minimal overcharge. Reconditioning is a process of restoring reserve capacity lost on cycling, which is commonly called the memory effect in NiCd cells. This effect is characterized by decreases in the discharge voltage curve with operational life and cycling. The end effect of reconditioning NiH2 cells may be hidden in the versatility, of that design over the NiCd cell design and its associated negative electrode fading problem. The process of deep discharge at lower rates by way of reconditioning tends to redistribute electrolyte and water in the NiH2 cell electrode stack, while improving utilization and charge efficiency. NiH2 battery reconditioning effects on life are considered beneficial and may, in fact. extend life based on NiCd experience. In any case, usable capacity data obtained from reconditioning is required for performance evaluation and trend analysis. Characterization and life tests have provided the historical data base used to determine the need for reconditioning in most battery applications. The following sections briefly describe the background of NiH2 battery reconditioning and testing at Lockheed Martin Missiles & Space (LMMS) and other aerospace companies.

Armantrout, J. D.↗

DOE OSTI2021

Interfacial Atomistic Mechanisms of Lithium Metal Stripping and Plating in Solid–State Batteries

All-solid-state batteries based on a Li metal anode represent a promising next-generation energy storage system, but are currently limited by low current density and short cycle life. Further research to improve the Li metal anode is impeded by the lack of understanding in its failure mechanisms at lithium–solid interfaces, in particular, the fundamental atomistic processes responsible for interface failure. Here, using large-scale molecular dynamics simulations, the first atomistic modeling study of lithium stripping and plating on a solid electrolyte is performed by explicitly considering key fundamental atomistic processes and interface atomistic structures. In the simulations, the interface failure initiated with the formation of nano-sized pores, and how interface structures, lithium diffusion, adhesion energy, and applied pressure affect interface failure during Li cycling are observed. By systematically varying the parameters of solid-state lithium cells in the simulations, the parameter space of applied pressures and interfacial adhesion energies that inhibit interface failure during cycling are mapped to guide selection of solid-state cells. Furthermore, this study establishes the atomistic modeling for Li stripping and plating, and predicts optimal solid interfaces and new strategies for the future research and development of solid-state Li-metal batteries.

25 ENERGY STORAGE↗

NASA NTRS1991

Thermal modeling of a Ni-H2 battery cell

The nickel-hydrogen secondary battery has many desirable features which make it attractive for satellite power systems. It can provide a significant improvement over the energy density of present spacecraft nickel-cadnium batteries, combined with longer life, tolerance to overcharge and possibility of state-of-charge indication. However, to realize these advantages, accurate thermal modeling of nickel-hydrogen cells is required in order to properly design the battery pack so that it operates within a specified temperature range during the operation. Maintenance of a low operating temperature and a uniform temperature profile within the cell will yield better reliability, improved cycle life and better charge/discharge efficiencies. This research has the objective of developing and testing a thermal model which can be used to characterize battery operation. Primarily, temperature distribution with the heat generation rates as a function of position and time will be evaluated for a Ni-H2 cell in the three operating modes: (1) charge cycle, (2) discharge cycle, and (3) overcharge condition, if applicable. Variables to be examined include charging current, discharge rates, state of charge, pressure and temperature. Once the thermal model has been developed, this resulting model will predict the actual operating temperature and temperature gradient for the specific cell geometry to be used.

Ryu, Si-Ok↗

NASA NTRS2023

Fighting Wildfires Using UAVs Using Autonomous Biodegradable Self-sacrificing (ABS) Drones to Combat Large Wildfires with the Assistance of Satellite Imagery

As wildfires continue to increase in number and severity due to global warming, firefighters are having a harder time combating them safely. This paper examines the application of Autonomous Biodegradable Self-sacrificing (ABS) Drones as a method of fighting wildfires that decreases the number of lives put at risk and proposes a sustainable alternative to current aerial firefighting methods. When a fire is detected or predicted through satellite imagery, ABS Drones are dispatched from firefighting stations or aerial watchtowers to fly towards designated areas and release fire retardants, preventing the fire from further spreading. The most significant feature of this drone is its ability to complete its mission without having to worry about returning safely because ABS Drones are designed to crash and release fire retardants, while not further harming the environment with water-based lithium-ion batteries and a biodegradable body. ABS Drones make it safer for firefighters as it gives firefighters one less life-threatening task to do and allows them to focus on putting out the fire. This solution aims to tackle the unpredictable nature of wildfires that often threaten the safety of firefighters and civilian communities.

ABS Drones↗

DOE OSTI2024

Evaluation of survival estimates generated from tracking downstream migrating juvenile sea lamprey ( Petromyzon marinus ) with a miniature acoustic telemetry tag

Fish that undertake juvenile migration pass through habitats that vary in mortality risk. The ability to identify regions of persistently low survival would improve fishery management. We conducted a field study combined with predictive modeling of survival in downstream migrating juvenile sea lamprey using a new micro-acoustic telemetry tag designed for implantation into small, slender-bodied fishes. Detection data were collected from eight receivers placed across a coastal riverine–wetland–lake complex. Juvenile sea lamprey initiated downstream movement near nautical twilight, stopped frequently, and were more likely to move during high river discharge. Estimated survival was highest in the riverine reach, declined through the river–wetland complex, and dropped precipitously in the drowned rivermouth lake. However, the high transmission rate and resulting short battery life of the microtransmitters (as configured) likely resulted in missed detections in the lower reaches. Simulation analyses suggested survival estimation could be improved by increasing the number of tagged lamprey and staggering release locations. We offer practical recommendations for the use of this new transmitter in field studies with small anguilliform fish.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

North America’s Potential for an Environmentally Sustainable Nickel, Manganese, and Cobalt Battery Value Chain

The Detroit Big Three General Motors (GMs), Ford, and Stellantis predict that electric vehicle (EV) sales will comprise 40–50% of the annual vehicle sales by 2030. Among the key components of LIBs, the LiNixMnyCo1−x−yO2 cathode, which comprises nickel, manganese, and cobalt (NMC) in various stoichiometric ratios, is widely used in EV batteries. This review reveals NMC cathodes from laboratory research. Furthermore, this study examines the environmental effect of NMC cathode production for EV batteries (including coating technologies), encompassing aspects such as energy consumption, water usage, and air emissions. Although gaps persist in NMC cathode environmental assessments (NMC111, NMC532, NMC622, and NMC811), limited life cycle assessments “(LCA)” have been conducted. Most available data originate from Asia (primarily China), accounting for 85% of the production of EV LIB cathode materials. The concept of battery passports for data collection on LIB components has been proposed to facilitate material traceability as a system for ensuring a sustainable supply chain for critical minerals. The automotive industry’s shift to electrification necessitates a sustainable supply chain from mine to vehicle end-of-life. As the critical mineral supply moves from Asia to North America, environmentally friendly industrial methods must be studied to provide this supply chain direction.

25 ENERGY STORAGE↗

NASA NTRS2018

Systems Health Monitoring and Prognostics Using Model Based Approach

In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system as well as subsystem levels. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. In addition a digital twin concept is being implemented in the framework to demonstrate verification and validation of developed algorithms.

Electronics Prognostics↗

DOE OSTI2023

Multi-layered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. As a result, case studies and a comparative analysis prove the efficacy of the proposed framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Electrochemical leaching of spent LIBs: Kinetics, novel reactor, and modeling

The use of electrons as main reagent for the recovery and recycling of critical metals from spent lithium-ion batteries (LIBs) is a process electrification strategy that can be used to close the life-cycle loop of LIBs through more sustainable methods. Electrochemical leaching, a process that uses a reductant that is constantly regenerated electrochemically for the leaching of lithium-ion battery black mass (LIBBM), has shown high extraction efficiencies and sustainable scores. However, slow kinetics, reactor design challenges and lack of deeper understanding of the underlying processes are barriers to the optimization, scale-up, and market adoption of this technology. In this paper, a kinetic study and mathematical model for dissolving LIBBM is presented to better understand the underlying mechanisms aiming to reduce the processing time and make predictions for future design and scale-up. The effect of acid and electrochemically mediated reductant concentrations, LIBBM loading, and cathode/reactor designs were explored. As a result, the leaching time was reduced from 7h to under 1h at a pulp density of 73 g/L, without external heating. A novel reactor with parallel baffle electrodes (PBE) was developed, which significantly reduced the leaching time by improving convection in a stirred slurry electrochemical reactor. Dimensionless numbers were deduced from an unsteady state model, which can be used in dimensional analysis for future process design and scale-up.

25 ENERGY STORAGE↗

NASA NTRS2017

Physics-of-Failure Approach to Prognostics

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Kulkarni, Chetan S.↗

DOE OSTI2025

Sustainable recovery of critical metals from spent lithium-ion batteries through gluconic acid-based bioleaching: Techno-economic analysis, life cycle assessment and process optimization

Recycling spent lithium-ion batteries (LIB) could potentially bridge the ever increasing supply and demand gap for critical metals and simultaneously facilitate the management of hazardous battery waste. This study investigated the optimization of gluconic acid-based bioleaching technology through design of experiments (DOE), combined with techno-economic analysis (TEA), and life cycle assessment (LCA) with the aim of maximizing the net present value (NPV) and minimizing global warming impacts of the process. Biolixiviant containing predominantly gluconic acid produced by the genetically engineered (ΔpstS, P 112 :mgdh) Gluconobacter oxydans B58 through fermentation using non-recyclable paper as a growth substrate was used for the LIB leaching. At optimal bioleaching conditions of gluconic acid (160 mM), leaching time (2.5 h), reducing agent FeSO 4 to metal, i.e., cobalt (Co), nickel (Ni) and manganese (Mn), mole ratio (0.88), temperature (55 °C) and pulp density (2.5 %), the leaching efficiency was 87 % 72 %, 94 %, and 88 % for Co, Ni, Mn and lithium (Li), respectively. TEA analysis confirmed that bioleaching plant with an annual black mass processing capacity of 10,000 metric tons and plant life of 30 years would be economically viable with an NPV and profit margin of $136 million and 11 %, respectively. The predicted carbon footprint of gluconic acid-based bioleaching for recovering 1 kg of Co (13.2 kg of CO 2 eq.) is lower compared to that of most state-of-the-art leaching technologies. Moreover, gluconic acid-based bioleaching effectively recovered target metals when tested for different black mass chemistries.

Bioleaching↗

NASA NTRS2016

The Global Precipitation Measurement (GPM) Spacecraft Power System Design and Orbital Performance

The Global Precipitation Measurement (GPM) spacecraft was jointly developed by National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA). It is a Low Earth Orbit (LEO) spacecraft launched on February 27, 2014. The spacecraft is in a circular 400 Km altitude, 65 degrees inclination nadir pointing orbit with a three year basic mission life. The solar array consists of two sun tracking wings with cable wraps. The panels are populated with triple junction cells of nominal 29.5% efficiency. One axis is canted by 52 degrees to provide power to the spacecraft at high beta angles. The power system is a Direct Energy Transfer (DET) system designed to support 1950 Watts orbit average power. The batteries use SONY 18650HC cells and consist of three 8s x 84p batteries operated in parallel as a single battery. The paper describes the power system design details, its performance to date and the lithium ion battery model that was developed for use in the energy balance analysis and is being used to predict the on-orbit health of the battery.

Power System Design and Orbital Performance↗

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↗

NASA NTRSDate not supplied

Spacecraft Fire Safety Predictions using Verified Saffire Model

A model developed using Fire Dynamics Simulator (FDS) that aimed to determine the effect of a fire in a spacecraft was validated by data collected during the Saffire campaign. The model used inlet and outlet temperatures and CO 2 concentrations of the Saffire payload where fire spread was taking place to determine the amount of heat and combustion products that made it into Northrop Grumman’s Cygnus vehicle. The model was then validated using six remote sensors in various places in the vehicle, as well as a far field device (FFD) in the open zenith section that was representative of average vehicle values. The current work focuses on using the model to predict fire safety scenarios. One simulation aimed to determine the fate of HCl, which sticks to surfaces. The model prediction showed that the HCl was removed from the atmosphere rapidly. This compared well against the FFD data in the Saffire VI campaign event where a bottle of 5% HCl was released into the vehicle. An additional simulation where the Environmental Control and Life Support System (ECLSS) was shut off once the FFD reached 5 ppm of HCl showed that HCl stayed in the atmosphere considerably longer. Continuing the simulation with the ECLSS activated and after temperatures returned to their initial conditions, resulted in a rapid removal of HCL similar to that observed in the original HCl release scenario model. Finally, a simulation that used the heat release rate from a lithium-ion battery test to determine the effect it would have on a spacecraft was performed. This simulation used a heat addition rate that is considerably higher than what was determined from the burning of solid fuels in the Saffire campaign and hence produced a non-trivial temperature increase in more locations within the vehicle.

Fire Safety↗

DOE OSTI2021

Battery asset management with cycle life prognosis

We report Battery Asset Management problem determines the minimum cost replacement schedules for each individual asset in a group of battery assets that operate in parallel. Battery cycle life varies under different operating conditions including temperature, depth of discharge (DOD), charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents a new battery asset management methodology where battery cycle life prognosis is integrated with parallel asset management to reduce lifecycle cost of the Battery Energy Storage Systems (BESS). For the battery failure time prognosis, a nonlinear physics-based battery capacity fade model is developed and incorporated in parallel asset management model to update battery capacity over time. Experiment results have shown that the developed battery asset management methodology can be conveniently used to facilitate BESS asset management decision making thereby decreasing asset lifecycle costs.

25 ENERGY STORAGE↗