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Batteries & storage

Find NASA NTRS and DOE OSTI reports about batteries and energy storage. Search electrode materials, lithium-ion batteries and storage systems in indexed report metadata.

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All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

Progress of Aluminum/Air Battery Development for SUSAN Electrofan Project

Safe and high-performance battery technologies are demanded by the Subsonic Single Aft eNgine (SUSAN) Electrofan concept design project under National Aeronautics and Space Administration (NASA) electrified aircraft development program. Aluminum (Al)-air battery with very high theoretical specific energy density, is considered as promising battery chemistry which has the potential to meet the targeted specific energy goal of primary battery energy technology for the SUSAN project. In addition to having the exceptional electrochemical properties, aluminum is light weight, rich abundance, low cost, environmentally friendly, and has a good electrical conductivity and recyclability, which is among the key characteristics of SUSAN project on battery energy needs. A non-flammable aqueous electrolyte is used to prevent the fire and thermal runaway hazard for the safety of this battery chemistry. However, there are challenges for Al-air battery in practical application, mainly due to self-corrosion of Al and slow kinetics of oxygen reduction in air-cathode to impact the high-rate performance. Progress on addressing these challenges was made and some of the results were reported in our previous paper. In this paper, the focus is on the investigation of various factors, including electrolyte concentrations, temperature, and air-cathode loadings on ionic transport properties and discharge performance at different current densities. The results are discussed, and the progress is reported.

James J Wu↗

DOE OSTI2018

Bulk force in a battery pack and its application to state of charge estimation

There is disclosed an electrical device including a battery pack, a pressure sensor for measuring a volume change of the battery pack, a voltage sensor in electrical communication with a positive terminal and a negative terminal of the battery pack, a temperature sensor positioned in a cell of the battery pack, and a battery management system. The battery management system includes a controller in electrical communication with the pressure sensor, the voltage sensor, and the temperature sensor, the controller being configured to execute a program stored in the controller to determine a state of charge percentage of the battery pack based on a pressure reading from the pressure sensor, a terminal voltage reading from the voltage sensor, and a temperature reading from the temperature sensor.

Stefanopoulou, Anna G.↗

DOE OSTI2019

Secondary battery management system

A method of managing a battery system, the method including receiving at least one measured characteristic of the at least one battery cell from the at least one sensor, estimating at least one state of the at least one battery cell at a first time by applying an electrochemical-based battery model, estimating at least one physical parameter of the at least one battery cell based on the at least one measured characteristic and the estimation of the at least one state, estimating the at least one state at a second time, subsequent to the first time, by applying the electrochemical-based battery model based on the estimated at least one parameter, and regulating at least one of charging or discharging of the at least one battery cell based on the estimation of the at least one state.

MirTabatabaei, Anahita↗

DOE OSTI2025

3D printed polymer gaskets for custom-form batteries

Additive manufacturing (AM) processes, like 3D printing, help to facilitate complex and customizable battery geometries which can provide design freedom and enhance volumetric energy density within electronic devices. AM materials must have the thermal and mechanical properties that enable printability, and when used in batteries, AM materials must also be chemically and electrochemically compatible with the battery chemistry. The compatibility between AM materials and the battery is of particular importance for the cell packaging materials which must be inert and are often overlooked. This study systematically studies AM-compatible polymeric materials for use as gaskets in lithium-ion cells. The materials investigated include three thermoplastics suitable for material extrusion printing: polylactic acid (PLA), polycarbonate, and polypropylene/polyethylene copolymer (PPPEC); and two photoresins suitable for vat photopolymerization (VPP) printing: an acrylate-based photoresin and a polyethylene glycol diacrylate photoresin. The AM gasket materials were tested in comparison to a conventional commercial polypropylene gasket. Mechanical testing (swell measurements and material stiffness) and electrochemical testing (linear sweep voltammetry and galvanostatic cycling of full cells) demonstrated that PLA and the VPP polymers were the least compatible with the lithium-ion battery chemistry, despite their prevalent use in studies of AM batteries, and that PPPEC was the most compatible.

3D printing↗

DOE OSTI2023

Impacts of Dispatch Strategies and Forecast Errors on the Economics of Behind-the-Meter PV-Battery Systems

To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncer- tainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.

batteries↗

DOE OSTI2016

Effect of the Anion Activity on the Stability of Li Metal Anodes in Lithium-Sulfur Batteries

With the significant progress made in the development of cathodes in lithium-sulfur (Li-S) batteries, the stability of Li metal anodes becomes a more urgent challenge in these batteries. Here we report the systematic investigation of the stability of the anode/electrolyte interface in Li-S batteries with concentrated electrolytes containing various lithium salts. It is found that Li-S batteries using LiTFSI-based electrolytes are more stable than those using LiFSI-based electrolytes. The decreased stability is because the N-S bond in the FSI- anion is fairly weak and the scission of this bond leads to the formation of lithium sulfate (LiSOx) in the presence of polysulfide species. In contrast, even the weakest bond (C-S) in the TFSI- anion is stronger than the N-S bond in the FSI- anion. In the LiTFSI-based electrolyte, the lithium metal anode tends to react with polysulfide to form lithium sulfide (LiSx) which is more reversible than LiSOx formed in the LiTFSI-based electrolyte. This fundamental difference in the bond strength of the salt anions in the presence of polysulfide species leads to a large difference in the stability of the anode-electrolyte interface and performance of the Li-S batteries with electrolytes composed of these salts. Therefore, anion selection is one of the key parameters in the search for new electrolytes for stable operation of Li-S batteries.

anion↗

DOE OSTI2025

Recent Progress in Solid‐State Lithium Batteries through Cathode Microstructure Engineering

A high‐performance cathode is necessary to realize the great potentials of solid‐state batteries such as high energy density and long cycle life. It is also needed to validate electrolyte performance, which is lacking. Currently, cathodes for solid‐state batteries are thinner and have lower cathode active material content than their lithium‐ion battery counterpart, resulting from insufficient conductivity and limiting the battery energy density. This review article provides an overview of recent development in cathode microstructures and their impact on battery properties, including compatibility between cathode and electrolyte, cathode architecture design, interface engineering, correlation between material properties, cathode processing approaches, and performance, as well as the advanced characterization methods used to understand the above correlations . Some perspectives on future development are shared including utilizing in situ and operando characterization tools to better understand dynamic evolution of the cathode/electrolyte interface, adapting artificial intelligence and machine learning to design and optimize cathode structures. The article is aimed to promote research interest on cathode development and advance solid‐state battery technologies.

cathode engineering↗

DOE OSTI2024

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Drone energy consumption↗

DOE OSTI2013

Recovery of manganese oxides from spent alkaline and zinc–carbon batteries. An application as catalysts for VOCs elimination

Highlights: • Manganese oxides were synthesized using spent batteries as raw materials. • Spent alkaline and zinc–carbon size AA batteries were used. • A biohydrometallurgical process was employed to bio-lixiviate batteries. • Manganese oxides were active in the oxidation of VOCs (ethanol and heptane). - Abstract: Manganese, in the form of oxide, was recovered from spent alkaline and zinc–carbon batteries employing a biohydrometallurgy process, using a pilot plant consisting in: an air-lift bioreactor (containing an acid-reducing medium produced by an Acidithiobacillus thiooxidans bacteria immobilized on elemental sulfur); a leaching reactor (were battery powder is mixed with the acid-reducing medium) and a recovery reactor. Two different manganese oxides were recovered from the leachate liquor: one of them by electrolysis (EMO) and the other by a chemical precipitation with KMnO{sub 4} solution (CMO). The non-leached solid residue was also studied (RMO). The solids were compared with a MnO{sub x} synthesized in our laboratory. The characterization by XRD, FTIR and XPS reveal the presence of Mn{sub 2}O{sub 3} in the EMO and the CMO samples, together with some Mn{sup 4+} cations. In the solid not extracted by acidic leaching (RMO) the main phase detected was Mn{sub 3}O{sub 4}. The catalytic performance of the oxides was studied in the complete oxidation of ethanol and heptane. Complete conversion of ethanol occurs at 200 °C, while heptane requires more than 400 °C. The CMO has the highest oxide selectivity to CO{sub 2}. The results show that manganese oxides obtained using spent alkaline and zinc–carbon batteries as raw materials, have an interesting performance as catalysts for elimination of VOCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Formulating energy density for designing practical lithium–sulfur batteries

We report the lithium–sulfur (Li–S) battery is one of the most promising battery systems due to its high theoretical energy density and low cost. Despite impressive progress in its development, there has been a lack of comprehensive analyses of key performance parameters affecting the energy density of Li–S batteries. Here, we analyse the potential causes of energy loss during battery operations. We identify two key descriptors (R weight and R energy ) that represent the mass- and energy-level compromise of the full-cell energy density, respectively. A formulation for energy density calculations is proposed based on critical parameters, including sulfur mass loading, sulfur mass ratio, electrolyte/sulfur ratio and negative-to-positive electrode material ratio. The current progress of Ah-level Li–S batteries is also summarized and analysed. Finally, future research directions, targets and prospects for designing practical high-performance Li–S batteries are proposed.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Preliminary design of betavoltaic battery using Co-60 and Pm-147 with GaAs substrate

Battery is very important for the present daily life, especially for portable devices. The longer utilization time the better performance of battery. Betavoltaic battery is a device that converts energy from beta decays of radioactive nuclide into electric current. One of merits of the later battery is the life time that can be more than ten years without recharging. To develop the betavoltaic battery for energy source of portable devices we have performed a preliminary simulation design of betavoltaic battery using Pm-147 and Co-60 a beta emitter radionuclides with n-GaAs substrate. From the results we found that the combination of Pm-147 with n-GaAs substrate results in 9.0% of efficiency and higher output current compared to references.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Investigation of Active Life Balancing to Recondition Li-ion Battery Packs for 2nd Life

An investigation is presented into the application of active life balancing battery management systems to recondition inhomogeneous Li-ion battery packs and achieve homogeneity in capacity for second life. This paper proposes a life balancing objective map, which controls the state of charge references for each cell in a battery pack based on its capacity relative to other cells. The impacts of life balancing objective maps on the reconditioning time and overall pack capacity fade are considered. Moreover, the proposed approach accounts for various load and environmental effects, including depth-of-discharge, battery pack temperature, number of cycles per day, and cells’ resting time at various states of charge. The study results in a battery pack with convergent capacities achieved in minimal time, while curtailing overall capacity fade. Simulation results for a battery pack with sixteen series connected 75 Ah Kokam NMC cells with 3.6% initial capacity imbalance are presented, indicating a reconditioning time of 1.34 months with a final capacity imbalance of 0.1% and an overall capacity fade of 0.005%.

47 OTHER INSTRUMENTATION↗

DOE OSTI2013

Electric Ground Support Equipment Advanced Battery Technology Demonstration Project at the Ontario Airport

The intent of the electric Ground Support Equipment (eGSE) demonstration is to evaluate the day-to-day vehicle performance of electric baggage tractors using two advanced battery technologies to demonstrate possible replacements for the flooded lead-acid (FLA) batteries utilized throughout the industry. These advanced battery technologies have the potential to resolve barriers to the widespread adoption of eGSE deployment. Validation testing had not previously been performed within fleet operations to determine if the performance of current advanced batteries is sufficient to withstand the duty cycle of electric baggage tractors. This report summarizes the work performed and data accumulated during this demonstration in an effort to validate the capabilities of advanced battery technologies. This report summarizes the work performed and data accumulated during this demonstration in an effort to validate the capabilities of advanced battery technologies. The demonstration project also grew the relationship with Southwest Airlines (SWA), our demonstration partner at Ontario International Airport (ONT), located in Ontario, California. The results of this study have encouraged a proposal for a future demonstration project with SWA.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2017

Modeling for Battery Prognostics

For any battery-powered vehicles (be it unmanned aerial vehicles, small passenger aircraft, or assets in exoplanetary operations) to operate at maximum efficiency and reliability, it is critical to monitor battery health as well performance and to predict end of discharge (EOD) and end of useful life (EOL). To fulfil these needs, it is important to capture the battery's inherent characteristics as well as operational knowledge in the form of models that can be used by monitoring, diagnostic, and prognostic algorithms. Several battery modeling methodologies have been developed in last few years as the understanding of underlying electrochemical mechanics has been advancing. The models can generally be classified as empirical models, electrochemical engineering models, multi-physics models, and molecular/atomist. Empirical models are based on fitting certain functions to past experimental data, without making use of any physicochemical principles. Electrical circuit equivalent models are an example of such empirical models. Electrochemical engineering models are typically continuum models that include electrochemical kinetics and transport phenomena. Each model has its advantages and disadvantages. The former type of model has the advantage of being computationally efficient, but has limited accuracy and robustness, due to the approximations used in developed model, and as a result of such approximations, cannot represent aging well. The latter type of model has the advantage of being very accurate, but is often computationally inefficient, having to solve complex sets of partial differential equations, and thus not suited well for online prognostic applications. In addition both multi-physics and atomist models are computationally expensive hence are even less suited to online application An electrochemistry-based model of Li-ion batteries has been developed, that captures crucial electrochemical processes, captures effects of aging, is computationally efficient, and is of suitable accuracy for reliable EOD prediction in a variety of operational profiles. The model can be considered an electrochemical engineering model, but unlike most such models found in the literature, certain approximations are done that allow to retain computational efficiency for online implementation of the model. Although the focus here is on Li-ion batteries, the model is quite general and can be applied to different chemistries through a change of model parameter values. Progress on model development, providing model validation results and EOD prediction results is being presented.

Prognostics↗

NASA NTRSDate not supplied

Usage-based Lifing of Lithium-Ion Battery with HybridPhysics-Informed Neural Networks

Lithium-ion batteries are commonly used to power unmanned aircraft vehicles (UAVs).The ability to model and forecast the remaining useful life of these batteries enables UAV reliability assurance. Building accurate models for battery state of charge and state of health based on first principles is challenging due to the complex electrochemistry that governs battery operations and computational complexity required to solve them. Therefore, reduced order models are often used due to their ability to capture the overall battery discharge. Un-fortunately, these simplifications lead to residual discrepancy between model predictions and observed data. In this paper, we present a hybrid modeling approach merging reduced-order models and neural networks. In this approach, while most of the input-output relationship is captured by Nernst and Butler-Volmer equations, data-driven kernels reduce the gap between predictions and observations. We validate our approach using data publicly available through the NASA Prognostics Center of Excellence repository. Results showed that our hybrid battery prognosis model can be successfully calibrated, even with a limited number of observations.

Lithium-ion Battery↗

NASA NTRSDate not supplied

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.

Battery Prognostics↗

NASA NTRSDate not supplied

Design and Analysis of Battery Thermal Management Systems

Thermal management of battery cell packs is a critically needed technology. The purpose of this work is to design new and improved Battery Thermal Management Systems (BTMS) for use in electric airplanes. The BTMS should be 3D printable and hold twelve to sixteen 18650 batteries. To minimize aircraft’s weight, an actively air-cooled battery pack was chosen to avoid the excess weight of water-cooled and phase change material (PCM) cooled packs. As a result, significant changes were made to remove all metal in the pack and replace it with lower density polymer matrix composites (PMCs) which can be additively manufactured. Two air cooled battery pack configurations (traditional propeller fans & bladeless fans) were designed, modeled, and compared. These packs were first modeled in SolidWorks 2021 3D CAD, then imported into COMSOL MultiPhysics to be studied using the “Heat Transfer in Solids and Fluids” module. The design with the bladeless fans eliminated the need to use high conductivity heavy metal to remove unwanted thermal energy. These bladeless fans were designed entirely out of PMCs. This thermal pack design weighs 0.04 kg less than that of the traditional propeller fans design and has increased the battery pack energy density by 8.25 Wh/kg.

Thermal Management System for Battery Packs↗