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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Electrolyte Reactivity at the Charged Ni-Rich Cathode Interface and Degradation in Li-Ion Batteries

The chemical and electrochemical reactions at the positive electrode–electrolyte interface in Li-ion batteries are hugely influential on cycle life and safety. Ni-rich layered transition metal oxides exhibit higher interfacial reactivity than their lower Ni-content analogues, reacting via mechanisms that are poorly understood. Here, we study the pivotal role of the electrolyte solvent, specifically cyclic ethylene carbonate (EC) and linear ethyl methyl carbonate (EMC), in determining the interfacial reactivity at charged LiNi 0.33 Mn 0.33 Co 0.33 O 2 (NMC111) and LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathodes by using both single-solvent model electrolytes and the mixed solvents used in commercial cells. While NMC111 exhibits similar parasitic currents with EC-containing and EC-free electrolytes during high voltage holds in NMC/Li 4 Ti 5 O 12 (LTO) cells, this is not the case for NMC811. Online gas analysis reveals that the solvent-dependent reactivity for Ni-rich cathodes is related to the extent of lattice oxygen release and accompanying electrolyte decomposition, which is higher for EC-containing than EC-free electrolytes. Combined findings from electrochemical impedance spectroscopy (EIS), TEM, solution NMR, ICP, and XPS reveal that the electrolyte solvent has a profound impact on the degradation of the Ni-rich cathode and the electrolyte. Higher lattice oxygen release with EC-containing electrolytes is coupled with higher cathode interfacial impedance, a thicker oxygen-deficient rock-salt surface reconstruction layer, more electrolyte solvent and salt breakdown, and higher amounts of transition metal dissolution. These processes are suppressed in the EC-free electrolyte, highlighting the incompatibility between Ni-rich cathodes and conventional electrolyte solvents. Finally, new mechanistic insights into the chemical oxidation pathways of electrolyte solvents and, critically, the knock-on chemical and electrochemical reactions that further degrade the electrolyte and electrodes curtailing battery lifetime are provided.

25 ENERGY STORAGE↗
DOE OSTI2025

Hierarchical analysis of US electric vehicle subsidies for carbon emission mitigation

Electric vehicle (EV) adoptions are promoted with subsidies to reduce greenhouse gas emissions from ground transportation. In this paper, a hierarchical analysis is presented on the potential of greenhouse gas emission mitigation via the electric vehicle subsidy policy at state level in the US, through research of environmental and economic fundamentals of electric vehicle operations, energy consumptions, battery degradation and service life. It has been found that restructuring the federal subsidies to promote EV adoption can significantly reduce greenhouse gas emissions across the US. The reduction costs of greenhouse gas emissions vary between $\$1167.44$/ton in Vermont to $\$6880.13$/ton in Wyoming. A case study reveals that 15.24 % more greenhouse gas emissions can be reduced with a tiered federal subsidy structure. The restructuring of subsidies will also encourage the adoption of clean energies in the grid fuel mix and drive technological advancements to extend the battery lifetime in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning

A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real-world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2% mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5% of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real-world conditions due to the impact of calendar degradation.

25 ENERGY STORAGE↗
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↗
DOE OSTI2020

Global Electrification of Vehicles and Intertwined Material Supply Chains of Cobalt, Copper and Nickel

Electric vehicles (EVs) will be an important part of a low-carbon economy. Future EV adoption depends on overcoming multiple barriers, including charging infrastructure, sufficient electricity generation, and the availability of battery raw materials at acceptable costs, including cobalt, copper and nickel – which typically are co-produced. Various EV projections have been made, along with associated requirements for battery raw materials (demand). There have been separate assessments of the availability of raw materials (supply). However, integrated assessments of supply and demand and their interaction over time are lacking to support decision-making. Using a dynamic market model that reflects the co-produced nature of cobalt, copper and nickel production as well as recycling, we estimate global output of these three metals under different EV growth scenarios from 2020 to 2040. In this work, results suggest that starting in 2025, replacement demand for battery raw materials becomes important. Thus, estimates for future material demand that consider only demands from new EVs are incomplete and misleading. EV deployment and associated increased demand for raw materials will have a greater impact on prices for cobalt than nickel and, in turn, for nickel than copper. High EV deployment scenarios will be difficult to achieve without larger supplies of copper, nickel and cobalt than forthcoming under the conditions modeled here, which anticipates a six-year time lag between a demand increase and the expansion of metal-production capacity. Shortening battery lifetimes and improving collection and recycling rates increase secondary supply, but the extent to which these factors support additional EV deployment is mixed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Improving the Long-term Cycle Performance of xLi 2 MnO 3 ·(1-x)LiMeO 2 /Li 4 Ti 5 O 12 Cells via Prelithiation and Electrolyte Engineering

Toward the development of high energy density and long lifetime batteries for behind-the-meter storage (BTMS) applications, Li- and Mn-rich layered oxide cathode (xLi 2 MnO 3 ·(1-x)LiMeO 2 , Me = Ni, Mn, and etc., LMR-NM) and Li 4 Ti 5 O 12 (LTO) anode system was examined. To mitigate the major degradation mechanisms at each electrode (i.e., loss of Li inventory (LLI) at the anode and transition metal dissolution and oxygen release at the cathode), two approaches were taken—prelithiating the LTO electrode and varying the electrolyte solvent compositions. The effect of prelithiation and electrolyte engineering on the long-term cycle performance of LMR-NM/LTO cells were systematically evaluated via electrochemical analyses and post-mortem characterizations. By using a prelithiated LTO anode and supplying additional Li to the system, the capacity retention of LMR-NM/LTO system was improved. The degree of enhancement was dependent on the types of electrolytes used, as their decomposition products determined the level of LLI. With increased capacity retention, however, the cathode was utilized to a greater extent, resulting in more severe loss of the cathode active material. Thus, all degradation mechanisms should be considered comprehensively when designing high performance LMR-NM/LTO cells to account for their complex interplay.

25 ENERGY STORAGE↗
DOE OSTI2023

Degradation and Modeling of Large-Format Commercial Lithium-Ion Cells as a Function of Chemistry, Design, and Aging Conditions

Demand for large-format (>10 Ah) lithium-ion batteries has increased substantially in recent years, due to the growth of both electric vehicle and stationary energy storage markets. The economics of these applications is sensitive to the lifetime of the batteries, and end-of-life can either be due to energy or power limitations. Despite this, there is little information from cell manufacturers on the sensitivity of cell degradation to environmental conditions or battery use. This work reports accelerated aging test data from four commercial large-format lithium-ion batteries from three manufacturers, with varying design (thickness, casings, ...), chemistry (lithium-iron-phosphate (LFP) or lithium-nickel-manganese-cobalt-oxide positive electrodes (NMC), with graphite (Gr) negative electrodes), and capacity (50 to 250 Amp hours). The tested LFP|Gr cell is found to be relatively insensitive to cycling conditions like temperature or voltage window, while NMC|Gr cells have varying sensitivity. Degradation trends are further investigated by training predictive models: simple polynomial trend lines, a semi-empirical reduced-order model, and an empirical reduced-order model identified using machine-learning based on symbolic regression. Calendar and cycle life are simulated over a variety of conditions to directly compare the various batteries. Cell size and thickness are found to substantially impact sensitivity to temperature during cycle aging, while electrode chemistry impacts depth-of-discharge sensitivity. Real-world battery lifetime is evaluated by simulating residential energy storage and commercial frequency containment reserve systems in several U.S. climate regions. Predicted lifetime across cell types varies from 7 years to 20+ years, though all cells are predicted to have at least 10 year life in certain conditions.

battery lifetime↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2022

Performance and Total Cost of Ownership of a Fuel Cell Hybrid Mining Truck

The main objective of this work was to investigate the potential of hydrogen and fuel cells replacing diesel and internal combustion engines in the ultraclass haul trucks deployed in the mining sector. Performance, range, durability, and cost are the main criteria considered for comparing the two fuels and engine options. Fuel cell system (FCS) performance is characterized in terms of heat rejection, efficiency, and fuel consumption for a hybrid platform equivalent to a 3500 hp diesel engine operating on a representative open pit mining duty cycle. A hybrid platform was chosen because the heat rejection, with a constrained radiator frontal area, limits the maximum fuel cell-rated power by about 50% compared to that of the diesel truck. The hybrid powertrain was 81–88% more efficient than the diesel powertrain on the truck duty cycle. A liquid hydrogen storage system is required for an equal range or time between refilling, but the packaging remains a challenge. Fuel cell and battery durability were evaluated for their performance degradation and lifetime. Achieving a fuel cell lifetime comparable to the time between major overhauls for diesel trucks necessitates the oversizing of the membrane-active area, catalyst overloading, and voltage clipping. For an equal lifetime, the battery must be oversized to control its depth of discharge and charge/discharge rates. A total cost of ownership (TCO) analysis considering the initial capital expenditures, as well as the lifetime cost of fuel, operation, and maintenance, indicates that fuel cells and hydrogen can compete with diesel. A breakeven fuel cost for TCO parity is obtained if H2 is available at USD 5.79–6.85/kg vs. diesel at USD 3.25/gal and the FCS-specific cost is USD 323/kW e relative to USD 250/kW for a diesel genset. Volume manufacturing is required for FCS cost reduction. High volume is possible through the standardization, modularity, and proliferation of class 8 long-haul truck systems across different heavy-duty applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime

Abstract Machine learning (ML)‐based approaches to battery design are relatively new but demonstrate significant promise for accelerating the timeline for new materials discovery, process optimization, and cell lifetime prediction. Battery modeling represents an interesting and unconventional application area for ML, as datasets are often small but some degree of physical understanding of the underlying processes may exist. This review article provides discussion and analysis of several important and increasingly common questions: how ML‐based battery modeling works, how much data are required, how to judge model performance, and recommendations for building models in the small data regime. This article begins with an introduction to ML in general, highlighting several important concepts for small data applications. Previous ionic conductivity modeling efforts are discussed in depth as a case study to illustrate these modeling concepts. Finally, an overview of modeling efforts in major areas of battery design is provided and several areas for promising future efforts are identified, within the context of typical small data constraints.

Sendek, Austin D.↗
DOE OSTI2023

Solid‐Adsorbed Polymer‐Electrolyte Interphases for Stabilizing Metal Anodes in Aqueous Zn and Non‐Aqueous Li Batteries

Abstract Polymers are known to adsorb spontaneously from liquid solutions in contact with high‐energy substrates to form configurationally complex, but robust phases that often exhibit higher durability than might be expected from the individual physical bonds formed with the substrate. Rational control of the physical, chemical, and transport properties of such interphases has emerged as a fundamental opportunity for scientific and technological advances in energy storage technology but requires in‐depth understanding of the conformation states and electrochemical effect of the adsorbed polymers. Here, we analyze the interfacial adsorption of oligomeric polyethylene glycol (PEG) chains of moderate sizes dissolved in protic and aprotic liquid electrolytes and find that there is an optimum polymer molecular weight of approximately 400 Da at which the highest columbic efficiency is achieved for both Zn and Li deposition. These findings point to a simple, versatile approach for extending the lifetime of batteries.

Jin, Shuo↗
DOE OSTI2023

Solid–Adsorbed Polymer–Electrolyte Interphases for Stabilizing Metal Anodes in Aqueous Zn and Non–Aqueous Li Batteries

Polymers are known to adsorb spontaneously from liquid solutions in contact with high-energy substrates to form configurationally complex, but robust phases that often exhibit higher durability than might be expected from the individual physical bonds formed with the substrate. Rational control of the physical, chemical, and transport properties of such interphases has emerged as a fundamental opportunity for scientific and technological advances in energy storage technology but requires in-depth understanding of the conformation states and electrochemical effect of the adsorbed polymers. Here, we analyze the interfacial adsorption of oligomeric polyethylene glycol (PEG) chains of moderate sizes dissolved in protic and aprotic liquid electrolytes and find that there is an optimum polymer molecular weight of approximately 400 Da at which the highest columbic efficiency is achieved for both Zn and Li deposition. Furthermore, these findings point to a simple, versatile approach for extending the lifetime of batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Tutorial: Machine Learning and Artificial Intelligence in Batteries

Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Uptake of Pb and the Formation of Mixed (Ba,Pb)SO 4 Monolayers on Barite During Cyclic Exposure to Lead-Containing Sulfuric Acid

Barite (BaSO 4 ) is a common additive in lead-acid batteries, where it acts as a nucleating agent to promote the reversible formation and dissolution of PbSO 4 during battery cycling. However, little is known about the molecular-scale mechanisms the nucleation and cyclic evolution of PbSO 4 over a battery’s lifetime. In this study, we explore the responses of a barite (001) surface to cycles of high and low lead concentrations in the presence of 100 mM sulfuric acid using in-situ atomic force microscopy and high-resolution X-ray reflectivity. Here we find that PbSO 4 epitaxial films readily nucleate on the barite surface, even from solutions that are undersaturated relative to bulk PbSO 4 . Despite this, barite (001) proves to be an ineffective nucleator of bulk PbSO 4 , as multilayer growth is suppressed even in highly supersaturated solutions. Instead, we find evidence that Pb 2+ ions can directly exchange with Ba 2+ to create mixed (Ba,Pb)SO 4 surfaces. These chemically mixed surfaces do not host PbSO4 monolayers as readily as pristine barite, and the original reactivity is not regained until a fresh surface is reestablished by aggressive etching. Our results can be partly explained by traditional models of Stranski-Krastanov (S-K) growth, in which monolayer films are stabilized by a reduction in surface energy, but multilayer growth is inhibited by epitaxial strain. Complementary density functional theory calculations confirmed the basic energetic-terms of traditional S-K models, but also showed evidence form more complex, thickness-dependent, energetics than would be predicted from the S-K models. The experimental results can be better understood by extending the S-K model to consider the formation of mixed surfaces and films, which can have reduced strain and interfacial energies relative to pure films, while also being stabilized by entropy of mixing. These insights into non-stoichiometric heteroepitaxy will enable better predictions of how barite affects PbSO 4 nucleation in battery environments.

25 ENERGY STORAGE↗
DOE OSTI2025

Physical Interpretation of Early Battery Life Prediction Models

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), d Q· d V −1 , and ΔV(SOC) curves. A thorough study on the dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and d Q· d V −1 models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.

25 ENERGY STORAGE↗
DOE OSTI2020

Electrolyte Lifetime in Aqueous Organic Redox Flow Batteries: A Critical Review

Aqueous organic redox flow batteries (RFBs) could enable widespread integration of renewable energy, but only if costs are sufficiently low. Because the levelized cost of storage for an RFB is a function of electrolyte lifetime, understanding and improving the chemical stability of active reactants in RFBs is a critical research challenge. We review known or hypothesized molecular decomposition mechanisms for all five classes of aqueous redox-active organics and organometallics for which cycling lifetime results have been reported: quinones, viologens, aza-aromatics, iron coordination complexes, and nitroxide radicals. We collect, analyze, and compare capacity fade rates from all aqueous organic electrolytes that have been utilized in the capacity-limiting side of flow or hybrid flow/nonflow cells, noting also their redox potentials and demonstrated concentrations of transferrable electrons. We categorize capacity fade rates as being “high” (>1%/day), “moderate” (0.1–1%/day), “low” (0.02–0.1%/day), and “extremely low” (≤0.02%/day) and discuss the degree to which the fade rates have been linked to decomposition mechanisms. Capacity fade is observed to be time-denominated rather than cycle-denominated, with a temporal rate that can depend on molecular concentrations and electrolyte state of charge through, e.g., bimolecular decomposition mechanisms. We then review measurement methods for capacity fade rate and find that simple galvanostatic charge–discharge cycling is inadequate for assessing capacity fade when fade rates are low or extremely low and recommend refining methods to include potential holds for accurately assessing molecular lifetimes under such circumstances. We consider separately symmetric cell cycling results, the interpretation of which is simplified by the absence of a different counter-electrolyte. We point out the chemistries with low or extremely low established fade rates that also exhibit open circuit potentials of 1.0 V or higher and transferrable electron concentrations of 1.0 M or higher, which are promising performance characteristics for RFB commercialization. We indicate important directions for future research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗