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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2014

A Testbed for Implementing Prognostic Methodologies on Cryogenic Propellant Loading Systems

Prognostics technologies determine the health state of a system and predict its remaining useful life. With this information, operators are able to make maintenance-related decisions, thus effectively streamlining operational and mission-level activities. Experimentation on testbeds representative of critical systems is very useful for the maturation of prognostics technology; precise emulation of actual fault conditions on such a testbed further validates these technologies. In this paper we present the development of a pneumatic valve testbed, initial experimental results and progress towards the maturation and validation of component-level prognostic methods in the context of cryogenic refueling operations. The pneumatic valve testbed allows for the injection of time-varying leaks with specified damage progression profiles in order to emulate common valve faults. The pneumatic valve testbed also contains a battery used to power some pneumatic components, enabling the study of the effects of battery degradation on the operation of the valves.

Prognositcs↗

DOE OSTI2020

Goldilocks and the three glymes: How Na+ solvation controls Na–O 2 battery cycling

In this work we report a framework to understand the role of solvent-salt interactions and how they mediate the performance of sodium-air/O 2 batteries. The utilization of suitable electrolyte materials remains a point of major concern within the research community, as their stability and decomposition pathways during cycling are intimately connected with capacity and cycle life. Glyme based solvents have been widely utilized in Na–O 2 batteries, however, to date no clear correlation between solvent/electrolyte properties and battery performance has been given. Herein, we have examined the effect of glyme chain length (ethylene glycol dimethyl ether DME; diethylene glycol dimethyl ether, DEGDME; and tetraethylene glycol dimethyl ether, TEGME) on the cycling behaviour of Na–O 2 batteries and conclude that overall cell performance is highly dependent on solvent selection, salt concentration and rate of discharge/charge. We demonstrate how solvent selection helps define cell chemistry and performance by linking salt-solvent interactions to enthalpy of dissolution - and subsequently to sodium battery electrolyte properties - through the combination of both experimental and theoretical methodologies. The approaches detailed in this study could be used to predictively prepare electrolytes for Li-air batteries, other glyme-based electrochemical systems and low temperature applications.

25 ENERGY STORAGE↗

NASA NTRS2017

A Generic Software Architecture For Prognostics

Prognostics is a systems engineering discipline focused on predicting end-of-life of components and systems. As a relatively new and emerging technology, there are few fielded implementations of prognostics, due in part to practitioners perceiving a large hurdle in developing the models, algorithms, architecture, and integration pieces. As a result, no open software frameworks for applying prognostics currently exist. This paper introduces the Generic Software Architecture for Prognostics (GSAP), an open-source, cross-platform, object-oriented software framework and support library for creating prognostics applications. GSAP was designed to make prognostics more accessible and enable faster adoption and implementation by industry, by reducing the effort and investment required to develop, test, and deploy prognostics. This paper describes the requirements, design, and testing of GSAP. Additionally, a detailed case study involving battery prognostics demonstrates its use.

Software Framework↗

NASA NTRS2020

A Generic Software Architecture for Prognostics

Prognostics and Health Management (PHM)↗

NASA NTRS1977

Coordinated NASA nickel-cadmium battery technology program

This paper describes the NASA nickel-cadmium (Ni-Cd) battery technology program which is being coordinated by JPL to provide NASA and other users with lighter and/or longer-lived Ni-Cd cells for low-earth-orbit, geosynchronous-orbit, and planetary spacecraft missions in the 1980s. The goal is to double the energy density and/or life of the system. Failure modes and mechanisms are differentiated and discussed to point out the critical design variables which affect Ni-Cd cell life. Cell component weights are listed, approaches for reducing weight are discussed, and program tasks and schedules are presented. The nine major tasks being worked on by various organizations are described. Progress has been made in weight reduction, understanding of life reliability mechanisms, and development of a process for manufacturing more stable electrodes. An accelerated and predictive life test program is approximately 30% complete. It appears feasible to double the life and/or usable energy density of the Ni-Cd cell.

Bogner, R. S.↗

DOE OSTI2025

Deciphering coulombic loss in lithium-ion batteries and beyond

Lithium-ion batteries are pivotal for modern energy storage, yet accurately predicting their lifespan remains a critical challenge. While descriptors like coulombic efficiency are widely used to assess battery longevity, the unclear physical origins of coulombic losses cause semi-quantitative correlation with capacity, complicating battery development. Here, we combine high-precision leakage current and open-circuit-voltage measurements with charge conservation principles to explore microscopic charge consumptions at electrode-electrolyte interfaces across diverse chemistries. We demonstrate that coulombic loss arises from a synergy between local charge neutrality and global charge compensation, reconciling its quantitative correlation to capacity. Contrary to conventional assumptions equating coulombic loss with irreversible capacity loss, this framework resolves systematic overestimations and paradoxical phenomena in existing chemistries. Our findings establish physics-informed criteria for accelerated lifespan evaluation and guide rational design of long-life lithium-ion batteries and beyond.

Batteries↗

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

Battery aging mode identification across NMC compositions and designs using machine learning

A comprehensive understanding of lithium-ion battery (LiB) lifespan is the key to designing durable batteries and optimizing use protocols. Although battery lifetime prediction methods are flourishing, diagnosis of the root causes of aging and degradation have not yet been well developed nor studied for a broad mixture of designs and use cases. Here, we create a machine-learning (ML)-based framework that distinguishes aging modes using multiple electrochemical signatures recorded cycle-by-cycle. The predominant aging behaviors include a combination of loss of active materials in cathode (LAMPE) and a loss of Li inventory (LLI) in Li plating or solid electrolyte interphase (SEI) formation, manifested from 44 batteries representing two cathode chemistries, two electrode loadings, and five charging rates. Here, the aging mode classification accuracy is 86% using features within the first 50 cycles and increases to 88% beyond 225 cycles. The same features can quantify the percentage of end-of-life LAMPE with only 4.3% of error.

25 ENERGY STORAGE↗

NASA NTRS2017

Thermal Assessment of Sunlight Impinging on OSIRIS-REx OCAMS PolyCam, OTES, and IMU-Sunshade MLI Blankets in Flight

The NASA Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer (OSIRIS-REx) spacecraft was successfully launched into orbit on September 8, 2016. It is traveling to a near-Earth asteroid (101955) Bennu, study it in detail, and bring back a pristine sample to Earth for scientific analyses. At the Outbound Cruise nominal spacecraft attitude, with Sun on +X, sunlight impinges on the OSIRIS-REx camera suite (OCAMS) PolyCam sunshade multilayer insulation (MLI) with microporous black polytetrafluoroethylene (PTFE), a portion of the PolyCam optics support tube (MLI with germanium black Kapton (GBK)), a portion of the OSIRIS-REx Thermal Emission Spectrometer (OTES) sunshade (MLI with GBK), the Inertia Measurement Unit (IMU) sunshade (MLI with GBK), and the OSIRIS-REx Laser Altimeter (OLA) sunshade (MLI with GBK). Sunlight is reflected or scattered by the above MLIs to the other components on the forward (+Z) deck. It illuminates the forward deck. A detailed thermal assessment on the solar impingement has been performed for the Proximity Ops at the asteroid, Touch-and-Go (TAG) sample acquisition, and Return Cruise mission phases.The OSIRIS-REx Outbound Cruise flight temperature telemetry and USM_3_DPC_0_CURRENT flight currenttelemetry data have been analyzed. It is evident that at the nominal Outbound Cruise spacecraft Sun-pointing attitude(i.e., Sun on +X), sunlight impinging on the PolyCam, OTES, IMU-sunshade and OLA-sunshade MLIs is reflected orscattered to the forward deck and components on the forward deck. It illuminates the forward deck. The StowCam imageof Day 265 2016 also provided an evidence. The reflected or scattered sunlight cause warming to the forward deck andcomponents on its +Z side. It may also contribute to degradation of thermal coatings over the mission life. It is a factorthat the OVIRS detector operating temperature exceeds the 105K maximum AFT limit. The OVIRS PrincipalInvestigator indicated that it is not optimum but acceptable for science. With exception of the OVIRS detector, thecorrelated flight system thermal model predictions for the components on the forward deck have adequate margins in theProximity Ops, TAG and Return Cruise phases. The margins are expected to cover the warming caused by the solarimpingement and the contribution to degradation of thermal coatings. The solar impingement is not expected to be athermal risk to the OSIRIS-REx mission. The second SRC Optical Properties characterization will be repeated in theReturn Cruise to provide a good characterization of any changes in optical properties that might have occurred duringthe TAG, or during several years in space. If the SRC battery runs much warmer than that of the first characterization inthe Outbound Cruise, it will be necessary to make some changes to the SRC Release timeline to assure the SRC batterytemperature are within limits. If GBK, instead of microporous black PTFE, were used on the PolyCam sunshade MLI,much more sunlight would have been reflected or scattered to the forward deck and components on its +Z side.Microporous black PTFE should be considered to mitigate the optical and thermal issues of sunlight reflected/scatteredby MLI blankets in future missions.

Choi, Michael K.↗

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 OSTI2020

Development of the Intelligent, Preventive Infrared (IR) Inspection System Housed in Hybrid Robotic Platforms

Robots and robotic systems that are designed for inspection, environmental study, and health and safety aid are becoming an increasing necessity. However, there is a number of challenges that accompany robots that are designed for these specific applications. These challenges include: navigating compact, enclosed spaces, travelling over multiple terrains and large obstacles, using the proper sensing and detection methods to assess an environment, and the use of lightweight and durable materials. The robotic platforms currently in development look at all of these challenges and attempt to overcome them. These designs specific use of hybrid robotic platforms, or platform that utilizes soft and rigid materials, allows for a more flexible platform and makes environments more navigable. To further improve the navigation of the platforms and environmental assessment, a novel infrared detection system is housed in the platforms to create a robot that can be used for the applications listed above and more. Objectives: Further develop two types of robotic platforms that utilize additive manufacturing, soft materials, and rigid materials. Continue the development of an intelligent and inhibitory infrared detection system based on an artificial intelligence (AI) algorithm. Continued study and fabrication of active soft materials designed for both sensing and actuation in hybrid robotic systems. Improve additive manufacturing fabrication to design rigid and semi-rigid components for hybrid robotic platforms. Transformable Wheel Robotic Platform: The chassis, wheels, tires and inspection system housing use different additive manufacturing techniques for fabrication. Continued work with additive manufacturing has lead to studies in metal-based printing and modular design and manufacturing. The new platform design with integrated electrical component printed. This will allow integration of the sensor housing onto the platform. Electrical components are being tested for battery life and performance. To improve this performance, such as integration of Lithium Polymer (LiPo) batteries. Snake Robotic Platform: The main focus of the development has centered around liquid-based soft actuators. That act on the principles of electrostatic and hydraulic actuation. A liquid dielectric sits between two compliant electrodes, contained by a flexible polymer shell. The electrodes and film gradually collapse toward each other from one corner of the electrode to the other. When the electrodes and film close together, a majority of the fluid is pushed into the area not covered by an electrode. A thin layer of the liquid dielectric remains between the electrode. The actuators will be stacked to cause large displacement, and move the linkages. The chassis of this platform uses purely additively manufactured linkages. Intelligent, Preventive IR Inspection System: Development of the AI for the system has lead to using a Scikit-Learn which assists in creating predictive models based on Regression, clustering, classification etc. To improve the infrared thermometry for low emissivity sources, work on the fabrication of a tandem photoconductive infrared thermometer was a main focus. Distance-Voltage-Temperature response data has been collected in the range of 7 cm - 100 cm and 200-400 deg. C. Modifications were made to the existing test bench to have a better control over the data. Automated data collection was realized using a Python code and an Arduino controlled stepper motor to increase the sample rate. A protective enclosure has been built around the setup to minimize the effect of the environment on the measurements. To better predict temperature, different AI models are being optimized. The regression model, LARS showed a high accuracy but had convergence issues and only works for the current test set-up. Results: The Transformable Wheel Robot has developed into a more flexible and modular platform. With the improvements to the current work, effort on the tire or soft gripper design been a large focus. The soft grippers will be interchange able to allow for increased performance in identified terrain types. The development of the liquid-based actuators allows the snake robotic platform to achieve the goals of being flexible and able to navigate confined spaces. However, there is room for improvement. Optimization work is currently being done in COMSOL Multiphysics. With the current IR system set-up the LARS model perfectly predicts the data; however, considering the mobility aspect of the project other models will allow for an optimized system. Testing of other model types is currently being done.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Enhanced polymorph metastability drives glycine nucleation in aqueous salt solutions

Crystal nucleation from aqueous solutions influences countless geological, biochemical, astrophysical, environmental, and materials science–related phenomena, including ice formation, the manufacturing of active pharmaceutical ingredients, development of diseases such as Alzheimer’s and the origin of life itself. Understanding and controlling nucleation is essential for designing materials with specific properties, developing strategies to inhibit or promote crystallization in various contexts and preventing pathological aggregation in neurodegenerative diseases. Similar to the protein structure prediction problem—where a single amino acid sequence can in theory adopt one most stable conformation but in practice may sample multiple competing conformations—crystal nucleation faces a parallel challenge: the same chemical species can form diverse polymorphs under different environmental conditions (e.g., temperature, pressure, solvent). Each polymorph presents its own set of physical and chemical properties, highlighting the importance of understanding and controlling polymorph selection in fields ranging from pharmaceuticals to materials design. Despite advances in experimental and computational methods for studying phase transitions and polymorph stability, nucleation remains challenging due to its nanoscale nature. Furthermore, in practical settings, salts and impurities can further influence crystal nucleation in diverse contexts, from scaling in pipelines and desalination plants to the durability of concrete and the efficiency of battery materials. This can lead to the formation of polymorphs that may differ from the most stable phase in pure solutions. Or, even though the final structure might appear same irrespective of whether the environment contained impurities or not, the mechanism through which it was formed might be completely different and not intuitive.

Wang, Ruiyu [University of Maryland, College Park,↗

DOE OSTI2021

Development of Electro-chemical Battery Model for Plug-and-Play Eco-system Library

Energy storage components are fundamental to the concept of an Integrated Energy System (IES). They serve to store surplus energy during low-demand periods for later release when other IES components (i.e., Secondary Energy Source, Balance of Plant, etc.) would otherwise have to operate flexibly. This provision for storage avoids high-amplitude power ramps in these components thereby limiting thermal and mechanical stresses to their internals and providing for extended service life. This report describes a dynamic model that has been developed for an electrochemical battery. The lithium-ion (Li-ion) cell was selected as representative technology. The battery model was developed in the Dymola simulation environment and meets the requirements of the ecosystem plug-and-play library. The model accurately describes the electric dynamic response of a Li-ion battery for an imposed charging/discharging power profile. The corresponding physical limitations related to over-power scenarios, and the impact of the residual state of charge are accounted for in the model. A literature review of the major degradation processes affecting Li-ion batteries was performed. Given the purposes of the CTD-IES project, the progressive fade of the installed capacity, the reduction of the round-trip efficiency, and the limits on the number of charging/discharging cycles are aspects that need to be taken into account in techno-economic analyses. The modeling of these degradation phenomena becomes crucial when predictions over long time horizons (capacity expansion) are made. For each one of these phenomena, a brief description is given, and some figures to be implemented in the HERON optimization algorithm are presented.

25 ENERGY STORAGE↗

DOE OSTI2020

A 3D porous honeycomb carbon as Na-ion battery anode material with high capacity, excellent rate performance, and robust stability

Motivated by the synthesis of three-dimensional (3D) honeycomb carbon structures and the subsequent theoretical prediction of an energetically more favorable hexagonal carbon phase composed of 28 carbon atoms in the unit cell (hC28) with ordered pores, excellent mechanical properties, and metallic feature, we explore its potential for a Na-ion battery (NIB) anode material. Using density functional theory based calculations, we find that hC28 is a promising candidate whose specific capacity of 717 mAh/g is almost three times larger than that of hard carbon (~250 mAh/g). In addition, migration barrier of Na ions along the honeycomb channel is only 0.08 eV and the volume change during the charging/discharging process is merely 2.30%, which are much less than those of other carbon-based NIB anode materials. The average voltage is also low (0.36 eV) which can provide high operating voltage when connected to the cathode. Finally, these encouraging results would pave the way towards the development of hC28 as NIB anode with high capacity, excellent rate performance, low open-circuit voltage, and long-term cycle life.

36 MATERIALS SCIENCE↗

NASA NTRS2013

Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction

This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decisionmaking. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the the first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.

Sankararaman, Shankar↗

NASA NTRS2012

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

Daigle, Matthew J.↗

NASA NTRSDate not supplied

Molten Salt Electrolytes: Computational Analysis of Transport, Electrochemical Properties and Designing New Mixtures

Electric aircraft propulsion has gained a traction over the last decade due to possible high-energy density electrochemistries with reasonable cycle life and identification of non-flammable chemistries that can guarantee much better safety. Commercial Li-ion batteries cannot reach such high capacities because of weight limitations that arise from the widely used intercalation electrodes. Also, use of organic electrolytes make them highly susceptible to fire on exposure to air and humidity. Currently, use of Lithium metal anode along with conversion electrochemistries such as Li-O2 and Li-S are being pursued to achieve such high energy densities. Safer inorganic solid-state electrolytes, recently discovered Water-in-Salt electrolytes, molten salt electrolytes are some of the alternatives being pursued for a safer/non-flammable battery. Of these safer alternatives, molten-salt electrolytes offer some attractive properties: liquid at operating temperatures resulting in better electrode-wetting, stable interface with Li-metal anodes and good ionic conductivity; their only drawback being high operating temperatures. In this talk, we will discuss some of the computational studies, driven via atomistic simulations, of the properties of molten-salt electrolytes including transport mechanism and interface stability. We will also discuss predicting electrochemical properties of these high-temperature electrolytes. Further, we will discuss a thermodynamic approach to predicting new molten-salt eutectics with lower melting points.

Molten Salt Electrolytes↗