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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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Performannce of high energy/high power Li-ion cells in radiation environments
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Design Considerations for Practical Li-S Batteries for Electric Aviation
The development of high-energy, high-safety, and high-power batteries beyond electric automotive standards is critical for future electric aviation applications. Non-volatile solid-state electrolytes (SSE) offer many promising advantages over traditional flammable liquid electrolytes. Solid-state electrolytes may be an enabling technology for certain battery chemistries by preventing detrimental interactions with liquid electrolytes, for example prevention of the dissolution of intermediates in lithium-sulfur common in traditional liquid organic solvents. However, significant manufacturing challenges must be overcome before the adoption of such technology. This research was conducted to identify processing techniques for producing solid-state battery components with practical dimensions and weights for enabling high specific energy cells. Traditional research has identified a range of very high theoretical ionic conductivities but are impractical due to their instability or inability to be manufactured. Electrolytes explored in this study were produced as sulfide-polymer composites with densified thicknesses below 40 micron using a tape-casting technique and thermoplastic elastomer binder. The combination of high chemical compatibility and flexibility produced robust films that had moderate impact on the ionic conductivity but was capable of dramatically reducing the parasitic mass. Despite minimal conductivity losses, films were produced 10-15 times thinner than comparable bulk powder electrolytes thus leading to overall lower film resistance. Conductivity is maintained above 0.2mS/cm for composite electrolytes. Through the analysis of materials based on their physical properties, such as density, practical cells can be designed. Based on this, low density sulfide-solid-state electrolytes are a promising candidate for achieving the energy and safety metrics for electric aviation, however, further optimization of cathode microstructure and composite processing will be required to achieve the high power metrics and will be the focus of further studies. Additionally, fabrication challenges arise when utilizing metallic lithium foil, due to its soft nature, as overflow and mechanical short circuit can occur on assembly which remains a challenge to overcome before metallic lithium anodes can be fully realized.
COTS Li-ion Strategic Reserve Update
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Performance of Li-SeS2 Solid State Cathodes: Composition and Process Dependence
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Performance of High Energy and High Power Commercial Li-ion Cells for Space Missions
UNKNOWN
The Development of Advanced High Voltage, High Specific Energy and High Power Li-ion Cells with Improved Low Temperature Performance
No abstract provided
Mixed-Domain Charge Transport in S-Se Alloys as a Li-S Battery Cathode Material
Lithium-sulfur batteries are emerging candidate systems for the next-generation long-range electric aircraft application owing to their potential to deliver high energy density per weight. Their cathodes are to be made primarily with sulfur, but sulfur by itself has much too low electron conductivity to serve as a useful cathode. An idea that has been suggested to overcome this bottleneck is to alloy sulfur with selenium in the hope that electron mobility, which is thought to improve charge transport at some expense of increased weight and reduced energy density. However, understanding of these alloy structures and their transport mechanism is insufficient, and electron mobility at varying degrees of selenium content is not well charted, which are critical for determining the optimum selenium content and testing whether this strategy is generally feasible. The difficulty of computationally characterizing these alloy systems is rooted in the structure. The structures of both sulfur and selenium exhibit eight-atom rings that pack together in various orientations to form their respective crystals. For one, because these crystals (and presumably their alloys as well) feature multiple polymorphs such that their structural coherence is rather unclear. Secondly, these are also relatively large systems (32 atoms per cell) with low-symmetry, which complicate computation both in terms of accuracy and efficiency. Thirdly, such semi-molecular, semi-crystalline structures lead to a combination of band-transport characteristics and hopping-transport characteristics, each of which is a domain with its own physics and set of computational, theoretical challenges. In addition, there is a general dearth of experimental transport data for sulfur-selenium alloys. In this study, we make a comprehensive attempt to tackle this problem using a wide array of first-principles methods. We generate special quasirandom structures to simulate alloy structures, and compute their first-principles Raman spectra for comparison with experimental data to ensure their structural soundness. We then proceed to use recently developed, state-of-the-art tool (AMSET) in order to efficiently compute electronic mobilities under band transport of pure sulfur, pure selenium, as well as their alloys of various compositions at a reasonable accuracy. We then sample numerous dimer configurations of nearest-neighbor eight-atom-ring-pairs and compute electronic hopping rates between them, which yields hopping mobility. A combination of these efforts lead to a general mapping of electron transport behaviors throughout the alloy range. Preliminary results show that introduction of selenium into sulfur initially damages electron mobility due to disorder but eventually improves it beyond that of pure sulfur with additional selenium content, ultimately peaking at the pure-selenium limit. Band transport dominates for holes in sulfur and electrons in selenium, but in all other cases, hopping transport is dominant. Ongoing efforts include determination of charge concentration and conductivity, as well as performing multiphysics modeling to determine the optimum selenium content for best tradeoff between conductivity and energy density for aircraft range.
Ester and Carbonate-Based Low Temperature Electrolytes in High Specific Energy and High Power 18650 Li-Ion Cells for Future NASA Missions
No abstract provided
An Additively-Manufactured Li-Ion Battery Case that Prevents Thermal Runaway
No abstract provided
Health Monitoring and Prognostics in Li-ion Batteries
Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly 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. 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. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. 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. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.
Design Considerations for Practical Li-S Battery Components for Electric Aviaiton
The development of high-energy, high-safety, and high-power batteries beyond electric automobile requirements is vital for future electric aviation applications. The transition to non-volatile solid-state electrolytes (SSE) promises many advantages over traditional flammable liquid electrolytes, and may also be an enabling technology for next generation chemistries. However, significant manufacturing challenges must be overcome before the adoption of such technology. Electrolytes developed in this study were produced as sulfide-polymer composites with densified thicknesses between 20-30 microns using a tape-casting technique and elastomer binder [1]. The composites were determined to retain reasonable ionic conductivity with improved flexibility and scalability critical for practical manufacturing of such cells. Films were produced 10-15 times thinner than comparable bulk powder electrolytes and within the range of commercial polyolefin separators (25 micron) used in commercial liquid containing lithium-ion cells. Composite conductivities were maintained above 0.2mS/cm, which holds promise for future electric aviation applications. Processing techniques are investigated to further improve electrochemical performance.
Health Monitoring and Prognostics in Li-ion Batteries
Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly 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. 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. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. 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. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.