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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hydrogen-Battery Hybrid Energy System on Repurposed Offshore Platforms for Efficient Clean-Energy Transition

Due to the rising global energy demand and enhanced awareness of the environmental impact of fossil fuels, the Gulf of Mexico, traditionally known for oil extraction, offers a distinct chance to repurpose the existing offshore infrastructure. With the depletion of oil reserves, it is feasible to adapt previously utilized floating platforms for extraction to generate renewable energy, specifically through wind-generated power and hydrogen production. This adaptation seeks to promote a transport system that is more ecologically friendly in the future. Offshore wind turbines serve as the main energy source, with help from battery storage and hydrogen production to enhance the overall system performance, hydrogen creation, fuel, and electricity delivery for sustainable energy production. The system is divided into two distinct cases, each evaluated for cost, performance, and feasibility, with a focus on minimizing both the Levelized Cost of Energy (LCOE) and the Levelized Cost of Hydrogen (LCOH). The first case examines the integration of offshore wind turbines with hydrogen production. Excess electricity generated by wind turbines is directed toward hydrogen production via electrolysis. The hydrogen produced can be used as fuel for vehicles or transported to the shore via pipelines. The second case investigates a technology that combines wind turbines with battery storage. The batteries possess an ability to supply electricity for a continuous duration of 4 hours maximum each day. The main objective is to reduce the LCOE by considering the battery's charging and discharging cycles, together with the uncertain attributes of wind power and battery deterioration. The produced energy can be distributed for onshore applications or utilized for the purpose of offsetting offshore loads such as subsea oil and gas production, transportation, etc. The offshore hydrogen-battery hybrid system is improved via three advanced algorithms, Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). In Case 1, PSO improves hydrogen production by efficiently managing the electrolyzer’s power consumption, decreasing production costs significantly. Particle Swarm Optimization (PSO) is applied to improve the efficiency of the electrolyzer, reducing production costs and achieving an optimized CAPEX of $240.00 million (from an initial $300.00 million) and OPEX of $9.60 million per year. This system produces 4,720,000 kg of hydrogen annually, with a Levelized Cost of Hydrogen (LCOH) of $6.40/kg and an annual profit of $9.27 million. In Case 2, GWO effectively reduces the overall energy cost by improving the charge-discharge management of batteries, which extends battery life and optimizes their use. The second case focuses on integrating battery storage, optimized using the Grey Wolf Optimizer (GWO), which enhances battery charge-discharge cycles, extending battery life and lowering costs. This system achieves an optimized CAPEX of $204.80 million (from an initial $256.00 million) and OPEX of $9.29 million per year, producing 310883.39 MWh of electricity annually at a Levelized Cost of Energy (LCOE) of $86.13/MWh, with an annual profit of $6.25 million. The implementation of a comprehensive strategy results in a substantial reduction in costs, improved energy efficiency, and a dependable supply of both electric power and hydrogen, emphasizing the benefits of converting offshore oil platforms for clean energy transition. This study explores a clean strategy to enable cost-effective repurposing of offshore O&G platforms. Both cases highlight the economic and technical feasibility of transitioning offshore oil platforms to clean energy systems, demonstrating substantial cost reductions and reliable energy and hydrogen supplies for sustainable energy production.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2016

International Space Station Lithium-Ion Battery

The International Space Station (ISS) primary Electric Power System (EPS) currently uses Nickel-Hydrogen (Ni-H2) batteries to store electrical energy. The electricity for the space station is generated by its solar arrays, which charge batteries during insolation for subsequent discharge during eclipse. The Ni-H2 batteries are designed to operate at a 35 depth of discharge (DOD) maximum during normal operation in a Low Earth Orbit. Since the oldest of the 48 Ni-H2 battery Orbital Replacement Units (ORUs) has been cycling since September 2006, these batteries are now approaching their end of useful life. In 2010, the ISS Program began the development of Lithium-Ion (Li-Ion) batteries to replace the Ni-H2 batteries and concurrently funded a Li-Ion ORU and cell life testing project. When deployed, they will be the largest Li-Ion batteries ever utilized for a human-rated spacecraft. This paper will include an overview of the ISS Li-Ion battery system architecture, the Li-Ion battery design and development, controls to limit potential hazards from the batteries, and the status of the Li-Ion cell and ORU life cycle testing.

battery↗
DOE OSTI2021

A review of thermal physics and management inside lithium-ion batteries for high energy density and fast charging

Traditionally it has been assumed that battery thermal management systems should be designed to maintain the battery temperature around room temperature. That is not always true as lithium-ion battery (LIB) R&D is pivoting towards the development of high energy density and fast charging batteries. Therefore, it is necessary to have a comprehensive review of thermal considerations for LIBs targeted for high energy density and fast charging, i.e., the optimal thermal condition, thermal physics (heat transport and generation) inside the battery, and thermal management strategies. As the energy density and charge rate increases, the optimal battery temperature can shift to be higher than room temperature. In the first part of the review various sources of heat generation inside LIBs and various approaches to minimizing battery heat generation are summarized. The importance of heat of mixing due to ion diffusion during fast charging is also highlighted. To improve the temperature uniformity and avoid excessive internal temperature rise, heat transfer inside the battery needs to be enhanced, and reducing the thermal contact resistance between the electrodes and separator can significantly increase the effective thermal conductivity of batteries. In the second part of the review various challenges and latest developments related to thermal transport and properties of LIBs are discussed. Finally, a summary of latest advancement on smart control of internal temperature of LIBs is discussed as depending on the ambient temperature and the optimal temperature; the battery heat needs to be retained or dissipated to elevate or avoid temperature rise.

25 ENERGY STORAGE↗
DOE OSTI2022

Procedure for Assessing the Suitability of Battery Second Life Applications after EV First Life

Using batteries after their first life in an Electric Vehicle (EV) represents an opportunity to reduce the environmental impact and increase the economic benefits before recycling the battery. Many different second life applications have been proposed, each with multiple criteria that have to be taken into consideration when deciding the most suitable course of action. In this article, a battery assessment procedure is proposed that consolidates and expands upon the approaches in the literature, and facilitates the decision-making process for a battery after it has reached the end of its first life. The procedure is composed of three stages, including an evaluation of the state of the battery, an evaluation of the technical viability and an economic evaluation. Options for battery configurations are explored (pack direct use, stack of battery packs, module direct use, pack refurbish with modules, pack refurbish with cells). By comparing these configurations with the technical requirements for second life applications, a reader can rapidly understand the tradeoffs and practical strategies for how best to implement second life batteries for their specific application. Lastly, an economic evaluation process is developed to determine the cost of implementing various second life battery configurations and the revenue for different end use applications. An example of the battery assessment procedure is included to demonstrate how it could be carried out.

25 ENERGY STORAGE↗
DOE OSTI2023

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗
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

Autonomous Strategies for Improved Performance and Reliability of Li‐Ion Batteries

Abstract The battery community has devoted significant effort to improve the performance and reliability of Li‐ion batteries through the development of new materials. The integration of autonomous function provides an alternative strategy to further extend performance. Autonomous material systems mimic the ability of biological systems to self‐protect, sense, regulate, and heal in response to damage and other environmental changes. When incorporated in batteries, these materials enable autonomic restoration of battery performance upon degradation, as well as autonomic shutdown or extinguishment in response to thermal runaway. Successful strategies for improving Li‐ion battery performance include controlled release of microencapsulated functional additives and the integration of self‐healing or adaptive interlayers and binders. These methods have been effective for stabilizing the solid electrolyte interface, preventing battery fires, restoring electrode conductivity, and suppressing Li dendrite growth. Challenges and opportunities exist in the further application of autonomous strategies in the battery environment. A greater variety of encapsulated additives with precise triggered‐release are needed to address specific battery degradation mechanisms. Incorporation of self‐healing polymers in solid‐state batteries facilitates improvement in interfacial and mechanical stability. The integration of autonomous strategies with battery management systems potentially enables enhanced control over multiple battery cycling parameters.

Zhao, Lihong↗
DOE OSTI2023

Stress characterization for friction-stir-welded electric vehicle battery trays with application of neutron diffraction

The battery tray is an essential component that protects and controls battery-cell temperatures in electric and plug-in hybrid vehicles. The functional stress limit of the battery tray heavily depends on the residual stress acquired from the manufacturing process. Consequently, exceeding the stress limit of the battery tray during operation could compromise the battery-cell banks and may risk the vehicle's safety. Hence, understanding residual stress formation is vital for design and safety concerns. Here, in the current study, AA 6061-plates were friction stir welded to an A365 high-pressure die-cast battery tray to create sealed coolant channels in the battery tray. However, this multi-material lap friction stir weld introduces residual stress into the battery tray, resulting in distortion. This distortion was mitigated using burnishing or coining operations, though straightening the battery tray had initially unknown effects on the residual stress. Therefore, neutron diffraction was utilized to characterize residual stresses after straightening. The results indicate that the friction stir welding (FSW) operation generated residual stresses exceeding the yield strength of the material, consequently deforming the battery tray by ±3 mm from the pre-weld geometry. The burnishing operation reduced the residual stresses below the material's yield strength while restoring the tray to within ±0.75 mm of the pre-weld geometry. Similarly, the coining operation restored the battery tray to within ±0.75 mm of the pre-weld geometry, however, increasing the number of locations where the residual stress exceeds the yield strength of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2026

R&D GREET Battery Carbon Footprint Calculator

The Battery Carbon Footprint (CF) Calculator was developed to help U.S. battery manufacturers meet the carbon footprint reporting requirements of the EU Battery Regulation (EU) 2023/1542. The calculator incorporates several major battery carbon footprint frameworks, including the Joint Research Centre's Rules for the Calculation of the Carbon Footprint of Electric Vehicle Batteries (CFB-EV), RECHARGE's Product Environmental Footprint Category Rules for High Specific Energy Rechargeable Batteries for Mobile Applications (PEFCR), the Catena-X Product Carbon Footprint Rulebook (CX-PCF Rules), Battery Pass's Battery Carbon Footprint: Rules for Calculating the Carbon Footprint of the "Distribution" and "End-of-Life and Recycling" Life Cycle Stages, the Global Battery Alliance's Greenhouse Gas Rulebook: Generic Rules, Version 2.1, and the Ministry of Economy, Trade and Industry's draft Carbon Footprint Calculation Method for Automotive Batteries. The tool pairs these frameworks with foreground data from Argonne's R&D GREET models and integrates user-supplied background data covering battery manufacturing and supply chain activities. By bringing multiple international methodologies together in a single platform, the calculator enables manufacturers to evaluate product carbon footprints, improve data consistency, and prepare for evolving regulatory compliance and global market reporting requirements.

Zhang, Jingyi↗
DOE OSTI2020

Development and Validation of a Simulation Tool to Predict the Combined Structural, Electrical, Electrochemical and Thermal Responses of Automotive Batteries

Battery safety is one of the most important design factors of electrified vehicles (EVs) since battery failure may lead to a catastrophic consequence. To improve the safety of battery systems in EVs, battery behaviors under various abuse conditions should be well understood. One way to examine battery response under extreme conditions is to conduct abuse tests in different scenarios. They can provide first-hand pass/fail information on battery safety, but they are usually expensive and time-consuming, which is inconvenient during design iterations and optimization. On the other hand, computational modeling can predict the battery behaviors in a more efficient and cost-effective way, which becomes an important tool to evaluate battery safety. Modeling battery responses and failure is challenging since it involves multiple coupled physical processes, needs to consider complex material properties and is computationally intensive. This project aims to develop a practical simulation tool to predict the combined structural, electrical, electrochemical, and thermal responses of automotive batteries to crash-induced crush and short circuit and validate it for conditions relevant to automotive crash. Advanced material constitutive models will be utilized to capture the mechanical response of cell components. Moreover, methods will be developed to reduce computational complexity of the model and allow battery safety simulations to extend from cell-level to module/pack level with affordable computational resources.

25 ENERGY STORAGE↗
DOE OSTI2024

Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics

With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.

Nazaralizadeh, Solmaz (ORCID:0009000737004039)↗
DOE OSTI2022

Design and Performance of lithium-Ion Batteries for Achieving Electric Vehicle Takeoff, Flight, and Landing

Today, the burgeoning drive towards global urbanization with over half the earth’s population living in cities, has created major challenges with regards to intracity and intercity transit and mobility. This problem is compounded due to the fact that almost always urbanization and increase in standard of living drives individual automobile ownerships. Over 95% of automobiles are presently powered by some form of fossil fuel and as an unintended consequence, urban centers have also been centers for peak greenhouse gas emissions, a major contributor to global climate change. A revolutionary solution to this conundrum is flight capable electric automobiles or electric aerial vehicles that can tackle both urban mobility and climate change challenges. For such advanced electric platforms, energy storage and delivery component is the vital component towards achieving takeoff, flight, cruise, and landing. The requirements and duty cycle demands on the energy storage system is drastically different when compared to the performance metrics required for terrestrial electric vehicles. As the widely deployed lithium ion-based battery systems are often the primary go-to energy storage choice in electric vehicle related applications, it is imperative that performance metrics and specifications for such batteries towards areal electric vehicles need to be established. In this nascent field, there exists ample opportunities for battery material innovations, understanding degradation mechanism, battery design, development and deployment of battery control and management systems. Thus, this chapter comprehensively discusses battery requirements and identifies battery material chemistries suitable for handling aerial electric automobile duty cycles. The chapter also discusses the battery cell-level metrics pertaining to electrochemical, chemical, mechanical, and structural parameters. Furthermore, specific models for battery degradation, state of health (SOH), capacity and models for full cell performance and degradation are also discussed here. Finally, the chapter also discusses battery safety and future directions of batteries that would power these next generation urban electric aircrafts.

Amin, Ruhul↗
NASA NTRS1982

GPS on-orbit battery performance

Three batteries designed for the GPS system were evaluated. The batteries were wired together, and during the eclipse period were discharged through parallel diodes into a boost converter which then boosted the battery voltage up to the totally regulated bus voltage. Each battery, with a system life of approximately five years, had its own charger. During testing the battery was maintained or used on trickle charge. Data are presented for the battery capacity during the reconditioning. Raw data for each eclipse period and calculations for the average discharge voltage for each battery are given. Thermal cycling for one of the batteries and reconditioning cycles and regimes for all of the batteries are discussed. A reconditioning discharge curve is also given. The problem of temperature cycling in one of the batteries was resolved by redesigning the battery radiator system.

Kasten, J. L.↗
NASA NTRS2013

Li-Ion Battery and Supercapacitor Hybrid Design for Long Extravehicular Activities

With the need for long periods of extravehicular activities (EVAs) on the Moon or Mars or a near-asteroid, the need for long-performance batteries has increased significantly. The energy requirements for the EVA suit, as well as surface systems such as rovers, have increased significantly due to the number of applications they need to power at the same time. However, even with the best state-of-the-art Li-ion batteries, it is not possible to power the suit or the rovers for the extended period of performance. Carrying a charging system along with the batteries makes it cumbersome and requires a self-contained power source for the charging system that is usually not possible. An innovative method to charge and use the Li-ion batteries for long periods seems to be necessary and hence, with the advent of the Li-ion supercapacitors, a method has been developed to extend the performance period of the Li-ion power system for future exploration applications. The Li-ion supercapacitors have a working voltage range of 3.8 to 2.5 V, and are different from a traditional supercapacitor that typically has a working voltage of 1 V. The innovation is to use this Li-ion supercapacitor to charge Liion battery systems on an as-needed basis. The supercapacitors are charged using solar arrays and have battery systems of low capacity in parallel to be able to charge any one battery system while they provide power to the application. Supercapacitors can safely take up fast charge since the electrochemical process involved is still based on charge separation rather than the intercalation process seen in Li-ion batteries, thus preventing lithium metal deposition on the anodes. The lack of intercalation and eliminating wear of the supercapacitors allows for them to be charged and discharged safely for a few tens of thousands of cycles. The Li-ion supercapacitors can be charged from the solar cells during the day during an extended EVA. The Liion battery used can be half the capacity required for a nominal EVA. The small Li-ion battery can be divided into two parallel modules with independent charging ports that would allow the supercapacitors to charge one battery while the other is providing power to the rover or suit.

Jeevarajan, Judith↗
DOE OSTI2022

Feature engineering for machine learning enabled early prediction of battery lifetime

Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. Here, a mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.

25 ENERGY STORAGE↗
DOE OSTI2021

Polypeptide organic radical batteries

In only a few decades, lithium-ion batteries have revolutionized technologies, enabling the proliferation of portable devices and electric vehicles, with substantial benefits for society. However, the rapid growth in technology has highlighted the ethical and environmental challenges of mining lithium, cobalt and other mineral ore resources, and the issues associated with the safe usage and non-hazardous disposal of batteries. Only a small fraction of lithium-ion batteries are recycled, further exacerbating global material supply of strategic elements. A potential alternative is to use organic-based redox-active materials to develop rechargeable batteries that originate from ethically sourced, sustainable materials and enable on-demand deconstruction and reconstruction. Making such batteries is challenging because the active materials must be stable during operation but degradable at end of life. Further, the degradation products should be either environmentally benign or recyclable for reconstruction into a new battery. Here we demonstrate a metal-free, polypeptide-based battery, in which viologens and nitroxide radicals are incorporated as redox-active groups along polypeptide backbones to function as anode and cathode materials, respectively. These redox-active polypeptides perform as active materials that are stable during battery operation and subsequently degrade on demand in acidic conditions to generate amino acids, other building blocks and degradation products. Here, such a polypeptide-based battery is a first step to addressing the need for alternative chemistries for green and sustainable batteries in a future circular economy.

25 ENERGY STORAGE↗
DOE OSTI2023

Incombustible Polymer Electrolyte Boosting Safety of Solid–State Lithium Batteries: A Review

Lithium-ion batteries with their portability, high energy density, and reusability are frequently used in today's world. Under extreme conditions, lithium-ion batteries leak, burn, and even explode. Therefore, improving the safety of lithium-ion batteries has become a focus of attention. Researchers believe using a solid electrolyte instead of a liquid one can solve the lithium battery safety issue. Due to the low price, good processability and high safety of the solid polymer electrolytes, increasing attention have been paid to them. However, polymer electrolytes can also decompose and burn under extreme conditions. Moreover, lithium dendrites are formed continuously due to the uneven charge distribution on the surface of the lithium metal anode. A short circuit caused by a lithium dendrite can cause the battery to thermal runaway. As a result, the safety of polymer solid-state batteries remains a challenge. In this review, the thermal runaway mechanism of the batteries is summarized, and the batteries abuse test standard is introduced. In addition, the recent works on the high-safety polymer electrolytes and the solution strategies of lithium anode problems in polymer batteries are reviewed. Finally, the development direction of safe polymer solid lithium batteries is prospected.

25 ENERGY STORAGE↗
NASA NTRS2014

A Battery Health Monitoring Framework for Planetary Rovers

Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computaitonally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA.

prediction↗