Engineering topics
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.
Provided are batteries and fuel cells incorporating a stripline detector for use in nuclear magnetic resonance (NMR). The stripline batteries and fuel cells can be used for in situ NMR measurement of battery or fuel cell chemistry. Also provided are methods for measuring in situ battery and fuel cell NMR using the stripline batteries and fuel cells of the invention.
A method of operating a battery comprises discharging a cathode comprising manganese dioxide to within a 2nd electron capacity of the manganese dioxide at a C-rate of equal to or slower than C/10, recharging the battery, and cycling the battery during use a plurality of times. The cathode is in a battery, and the battery comprises the cathode, an anode, a separator disposed between the anode and the cathode, and an electrolyte. The cathode comprises the manganese dioxide and a conductive carbon. The anode comprises: a metal component and a conductive carbon. The metal component can be a metal, metal oxide, or metal hydroxide, and the metal of the metal component can be zinc, lithium, aluminum, magnesium, iron, cadmium and a combination thereof.
A method of battery state monitoring includes: (1) providing a battery cell and at least one ultrasonic actuator and at least one ultrasonic sensor mounted to the battery cell; (2) using the ultrasonic actuator, generating a guided wave that propagates in-plane of the battery cell; (3) using the ultrasonic sensor, receiving an arriving wave corresponding to the guided wave; and (4) determining a state of the battery cell based on the arriving wave.
Methods and systems are provided for transporting and hydrating a redox flow battery system with a portable field hydration system. In one example, the redox flow battery system may be hydrated with the portable field hydration system in a dry state, in the absence of liquids. In this way, a redox flow battery system may be assembled and transported from a battery manufacturing facility to an end-use location off-site while the redox flow battery system is in the dry state, thereby reducing shipping costs, design complexities, as well as logistical and environmental concerns.
Methods for diagnosing failure mechanisms and for predicting lifetime of metal batteries include monitoring rest voltage and Coulombic Efficiency over relatively few cycles to provide profiles that indicate, by the trends thereof, a particular failure mechanism (e.g., electrolyte depletion, loss of metal inventory, increased cell impedance). The methods also include cycling over relatively few cycles an anode-free cell, having the same cathode and electrolyte as the metal battery, but with a current collector instead of the anode. Discharge capacity is monitored and profiled, and a discharge capacity curve is fitted to the discharge capacity profile to discern a capacity retention per cycle. The lifetime of the metal battery is determined using the capacity retention per cycle discerned from the anode-free cell. Related systems include a metal-based battery and an anode-free cell or a battery cell reconfigurable between a metal-based and an anode-free cell.
A transient or biodegradable battery is provided having a filament structure that limits the speed of reaction allowing for a longer duration of battery power with a controlled current limit. In one embodiment, the filament may be constructed of zinc microparticles or nanoparticles having a thin outer insulation whereby a chemical reaction at the center core results in the progressive disintegration of the insulation revealing more core material. In one embodiment, microparticles or nanoparticles are coated with outer layers of chitosan and Al2O3 nanofilms, respectively, with designable discharge current and battery lifespan by controlling the exposed cross-sectional area of the zinc microparticle center core and the length of the filament, respectively. This novel structure of biodegradable battery provides improved control of battery life and power output, providing a promising solution to power transient medical implants.
This invention relates to novel battery separators comprising ceramic nanowires, more specifically, inorganic carbonate nanowires. The novel ceramic nanowire separators are suited for use in lithium batteries, such as lithium ion rechargeable, lithium metal rechargeable and lithium sulfur rechargeable batteries, and provide high safety, high power density, and long cycle life to the fabricated rechargeable batteries. The battery separators comprise ceramic nanowires that may be optionally bonded together by organic polymer binders and/or may further comprise organic nanofibers.
Lithium-oxygen (Li-O 2 ) batteries have attracted extensive research interest due to their high energy density. Other than Li 2 O 2 (a typical discharge product in Li-O 2 batteries), LiOH has proved to be electrochemically active as an alternative product. Here we report a simple strategy to achieve a reversible LiOH-based Li-O 2 battery by using a cation additive, sodium ions, to the lithium electrolyte. Without redox mediators in the cell, LiOH is detected as the sole discharge product and it charges at a low charge potential of 3.4 V. A solution-based reaction route is proposed, showing that the competing solvation environment of the catalyst and Li+ leads to LiOH precipitation at the cathode. It is critical to tune the cell chemistry of Li-O 2 batteries by designing a simple system to promote LiOH formation/decomposition.
In this work, we report the impact of the temperature-driven synergistically-coupled anodic degradation mechanisms on the electrochemical performance of lithium batteries with graphite anode over multiple cycles. Temperature dependence of electrochemical reactions and damage mechanisms, such as solid electrolyte interface (SEI) growth, lithium plating/stripping, dead lithium storage/dissolution, and film cracking are incorporated into the degradation model. Results of a parametric analysis are presented, evaluating the effects of charging rates (1–6 C), operating temperatures (-15 – 45°C) and electrode design parameters, on the relative performance fade in the lithium-ion battery. Thermo-electrochemical process maps are developed to provide insights into the relationship between electrode performance and failure mechanisms. The simulation results predict a severe capacity loss due to lithium plating at low temperatures, which is further aggravated at high charging rates. A common strategy for mitigating lithium plating, through charging at high temperatures, also results in rapid capacity loss due to accelerated SEI formation. Simulation results are used to identify the combination of operating conditions and electrode design parameters that improve the electrochemical performance of the battery. These results demonstrate an opportunity to design safe and high-performance lithium-ion batteries, guided by anodic degradation models.
Atomic-resolution imaging of battery materials is critical for identification of local defects and structural variations, which are tied to battery performance. However, since battery materials are, by design, optimized to allow ion motion in response to an applied electric field, they are also very sensitive to radiation damage by an electron beam. Image resolution is therefore severely constrained by the dose applied. Here, we show that multislice electron ptychography (MEP) can provide sub-ångström lateral resolution images of both light and heavy elements of a Li-ion battery cathode, along with nanometer-scale depth information and greater dose efficiency than conventional electron microscopy methods. Using the depth-sectioning capability of MEP, we have been able to obtain direct visualizations of Li vacancy clusters, atom column by atom column, in Li x Ni 0.33 Mn 0.33 Co 0.33 O 2 (NMC111) cathodes. This capability to track Li distributions will be valuable in understanding, informing, and optimizing electrode material design for ion storage and transfer.
During high current operation, substantial heterogeneity develops within battery cathodes, particularly when their thickness is large. Heterogeneity relaxation during subsequent rest is important for understanding battery performance under pulsed conditions. Localized charge balancing phenomena within batteries at zero net current are not well understood and merit investigation. In this work, the heterogeneity within cathodes of commercial alkaline Zn–MnO 2 batteries is measured during discharge and monitored during rest using energy dispersive X-ray diffraction (EDXRD). Significant gradients in protonation form during discharge and partially relax under rest. It is demonstrated that the proton gradient relaxation is through local redox activity at zero net current, where local (de)protonation works to redistribute charge across the cathode thickness. To support this redox-based relaxation, a fundamental kinetic study on prismatic MnO 2 cathodes is conducted to determine an appropriate model to describe both discharge and charge kinetics of MnO 2 . These kinetics are incorporated into a computational model to simulate the proton gradient formation and partial relaxation under identical discharge conditions as the operando EDXRD experiments. Model and experimental data are found to be in excellent agreement, correctly predicting localized charge balancing at rest.
The pervasive use of Ni-rich cathode active materials, e.g., LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811), for high-energy-density Li-ion batteries (LIBs) has been hindered by rapid battery capacity decay when cycled with high charge cutoff voltages due to electrolyte decomposition in the conventional carbonate solvent-based electrolytes, oxidative parasitic side reactions at the electrolyte/cathode interface, and irreversible phase changes in the cathode active materials leading to dissolution of transition metals into the electrolytes. Various functional electrolyte solvents have been studied to tackle the above technical challenges, yet the roles of individual solvents in the performance of LIBs remain poorly understood. Here, in this study, we systematically investigate electrochemical performance mechanisms of fluorinated and organosilicon single solvents and cosolvents, for the first time, in high-voltage Li/NMC811 batteries, using electrochemical and analytical characterizations and density functional theory modeling. We observe that some unique combinations of the functional solvents can lead to exceptionally stable high-voltage cycle performance in the Ni-rich cathode-based LIBs. Our mechanistic study reveals that the synergistic effect of solvents plays a vital role in enabling electrochemical stability at both the Ni-rich cathode and the Li metal anode. Understanding the electrochemical performance mechanisms of functional solvents can greatly help in designing and formulating advanced electrolytes that enable the development of high-voltage, high-energy-density, long-cycle-life lithium batteries.
Jurisdictions are increasingly adopting compensation structures for distributed PV and PV-battery systems that price exported energy lower than energy consumed onsite (net billing rates). Standard methods for calculating the bill savings from PV and PV-battery systems were developed for net metering structures, and applying these same methods to net billing structures (such as using Typical Meteorological Year weather with actual year load) introduces bias errors that underestimate PV-battery system bill savings by between 1.5\% and 9\%, depending on the utility rate. We assess the magnitude of these errors and compare them to other sources of uncertainty when estimating the bill savings from PV and PV-battery systems under more complex utility rates.
This case study analysis highlights the role of innovations in EV battery design (cells, modules, and packs), reverse supply chain, and recycling processes in yielding value for the economics of recycling (and have the largest impact on the circularity of LIBs). Part of the assessment of value will be a semi-quantitative evaluation of the value of resilience in the rapidly evolving LIB market – i.e., there are a variety of risks that recyclers would face in making a financial commitment to a recycling facility including; the possibility that batteries would not be collected in sufficient quantities, the market for key constituents (e.g., cobalt) might decrease because of changes in battery chemistry, battery manufacturers may not be willing to pay as much for recycled material, etc. The analysis would be semi-quantitative in that two simple models would be used to assess 1. material flows using a previously developed excel-based reverse supply chain flows model, and 2. A LIB recycling process material and energy balance cost model that will be used to qualitatively assess the cost impacts of process innovations and changes in feed streams. Both sets of models are highly speculative in that they rely on a multitude of assumptions and input values (e.g., EV adoption rates) that vary widely in the literature. Additionally, the initial process flow diagrams and equipment lists for the recycling cost model are based on the Argonne EverBatt model, which is still under development. However, in combination with a critical review of the literature, economic modeling can yield valuable insights into the role of innovation in the circularity of LIBs and high-technology (“energy relevant”) products in general.
Solid lithium peroxide (Li 2 O 2 ) is the major discharge product in Li-air batteries. However, the electronically insulating nature of Li 2 O 2 tends to affect the battery’s performance such as the polarization gap and cyclability. On the other hand, lithium superoxide (LiO 2 ), generated through a one-electron transfer process, offers greater electronic conductivity, lower charge transfer resistance, and thus reduced charge potential. Nevertheless, LiO 2 long-term stabilization as a final product remains a significant challenge. Here, in this study, we present the molybdenum (Mo)-based Janus chalcogenide family featuring asymmetric structures as a new generation of cathode catalysts for Li-air batteries. These catalysts demonstrate remarkable efficacy in stabilizing LiO 2 discharge products, even under high current densities of 5000 mA/g (corresponding to 0.5 mA/cm 2 ). Our density functional calculations provide an understanding of why the asymmetric Mo-Janus chalcogenides result in LiO 2 formation whereas the symmetric Mo-dichalcogenides produce Li 2 O 2 as the discharge product. These results pave the way to explore a new generation of advanced catalysts for superoxide-based Li-air batteries.
Accurately predicting Li-ion battery capacity trajectories using early-life data can dramatically improve battery-life understandings and be used to rapidly evaluate design/cost/performance trade-offs when developing new battery materials. Accurate early-life predictions enable researchers to quickly iterate over cell designs and material precursor properties without consistently cycling cells to failure. To this end, we present a toolbox that uses a combined Gaussian Process and Bayesian regression approach that capitalizes on signals other than just capacity (e.g., dQ/dV, voltage drops) to rapidly predict capacity-fade trajectories. The prediction tool uses Bayesian regression to fit functional forms, e.g., power law, sigmoids, etc., to predict capacity-fade dynamics. By fitting functional forms, the capacity fade can be interrogated at any point in the future, allowing for early cell-failure prediction. Additionally, Bayesian regression allows for accurate uncertainty estimates that account for cell-to-cell variability (aleatoric uncertainty) and the lack of observation data (epistemic uncertainty). By only using early cycle data to predict the capacity fade trajectory, uncertainty bounds at end-of-life can be extremely large. The large uncertainty bounds are further exacerbated because there is no systematic way to define the prior distribution of the functional forms' parameters. We improve our the predicted trajectory confidence interval of our predicted trajectory using two methods. First, we shows that a small amount of held-out cycling data is sufficientuse some train cells, that have been cycled to failure to derive information regarding the appropriate prior distributions for the functional forms' parameters of the functional form, effectively leading to data-driven priors.. We propose constructing the data-driven priors by first running a Bayesian regression starting with uninformed priors to generate intermediate cell-specific posterior parameter distributions. These posterior distributions are combined using a Ggaussian mixture model for each parameter to create the data-driven priors. These mixture models serve as the data-driven prior distributions for the parameters for. Second, we derive multiple features, e.g., C_dchg 0.5 DoD 0.5, log (|mean(dQ/dV_(w_3-w_0 ) (V)|), etc., from the train cellsheld-out cycling data, identify which the features are that best predicting capacity at early/mid-life cycles, and then create Ggaussian process regression models that are used for predicting capacity at early/mid-life cycles for the test cells (see blue dots with error bars in Fig 1b). Finally, these predicted data-points are used in addition to the actual early cycle data capacity fade to construct the Bayesian regression trajectory for the test cell s. Notably. We note that these two methods are complementary and can be combined with each other. We evaluate the performance of our proposed method on an testing open-source dataset from Iowa State University and Iowa Lakes Community College (ISU-ILCC). This dataset comprises of 251 nickel-manganese-cobalt/graphite Lithium-ion cells that are cycled under 63 different conditions. We compute the mean average percentage error (MAPE) and negative log predictive density (NLPD) to quantify the efficacy of our method. Our initial findings suggest that, when only few observations are available, for test cells, when using only Bayesian regression with uninformed priors, a power law functional provides the most accurate predictions. with very few data points. However, asHowever, a the number of data points increases, a twin sigmoidal function becomes more accurate as the number of observations further increases. We also find that using as little as 10% of the data set towards generating data-driven priors can lead to significant improvement in prediction accuracy when using early cycle data. Lastly, we found that augmenting early-cycle data with Gaussian process-predicted capacity data for Bayesian regression greatly improves the prediction accuracy. We will present a comprehensive comparison of our methods to other methods available in the literature and apply this method to additional battery datasets.
All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes and protective coatings simultaneously possessing high ionic conductivity and wide electrochemical stability has proven to be a challenge. Here, we present a data-driven approach to explore the Li compound space for promising solid electrolytes and coatings. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds by computing Li+ migration barriers using bond-valence-based pair potentials, and stability windows using density functional theory energies. Using this database, we implement machine learning models that can accurately predict migration barriers and electrochemical stability windows for any new Li compound. Through feature engineering, we ensure that our models are both accurate and interpretable. We perform feature importance analysis on our models to highlight materials properties that can be tuned for future design of coatings/electrolytes. Our database and informatics approach provide a valuable tool for the rapid discovery of new solid-state battery chemistries.
Lithium metal batteries are prone to subtle defects such as internal dendrites, which can cause internal short circuits and lead to catastrophic ignition. These defects are often undetectable by battery management systems, prompting the need to advance nondestructive evaluation (NDE) techniques for battery applications. In this work, two ultrasonic approaches were compared to establish their relative merits for this application: local ultrasonic resonance spectroscopy, and guided wave ultrasound. Benchtop experiments on batteries seeded with lithium chips emulating plating were used to demonstrate the two techniques. The results show the ability to identify and map local changes in cell structure.