Impact of Aging on the Safety of Lithium-ion Batteries
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Preger, Yuliya↗
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Impact of Aging on the Safety of Lithium-ion Batteries
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Preger, Yuliya↗
Impact of Aging on the Safety of Lithium-Ion Batteries and Implications for Diagnostics
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Preger, Yuliya [Sandia National Laboratories (SNL-↗
Facile Electrodeposition and Aging to Generate 3-Dimensional α-MnO 2 Battery Cathodes
Conventional tape casting forms 2-dimensional (2D) electrodes containing active material, conductive additive, and binder with restricted ion access as electrodes increase in thickness. To improve the transport properties, 3D architectures were developed using electrodeposition to ensure contact between the active material with the substrate, and provide enhanced electrolyte access. This paper investigates electrodeposition of cryptomelane ( α -MnO 2 ) as a model cathode material to efficiently accommodate (de)lithation and increase areal capacity vs conventional 2D coatings. Electodeposited samples on titantium (Ti) foil substrates were characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) and show a linear increase of the average oxidation of Mn (3.5–3.8) and active mass loading (1.27–9.9 mg) with deposition and aging times (0–120 min). The initial deposition is amorphous and forms the crystalline material during the elevated temperature aging step. The active material, α -MnO 2 , was also deposited on C-cloth and these cathodes at deposition times of 3, 6, and 9 min deliver 9, 36, and 69% higher areal capacities, respectively, at 0.2 mA cm −2 compared to conventional 2D electrodes with a mass loading equal to the 3 min sample. These results demonstrate the benefit of α -MnO 2 within a porous architecture providing enhanced transport properties.
Electrochemistry↗
Equipment Qualification Report Environmental Qualification of GNB Absolyte Valve Regulated Lead Acid (VRLA) 1600 Ah100G33 Battery Rack Assembly (24590-QL-POA-EDB0-00001-11-00002_00A)
Greenberry Environmental Qualification Report 550001.001-35.0.5 provides basis for assignment of qualified life for: GNB Absolyte Valve Regulated Lead Acid (VRLA) 1600 Ah 100G33 Battery Rack Assembly in accordance with the requirements specified 24590-WTP-3PS-G000-T0015 (Rev 2) and Environmental Qualification Plan 550001.001-35.0.1 (Rev. 3). The equipment qualification basis represents the most conservative capability of the equipment. The analysis performed for the qualification is not less conservative than the bounding environmental conditions detailed in contract documents issued to Greenberry in contract 24509-QL-POA-EDB0-00001 Rev.0. The qualified life of 10 years has been established based upon an end-condition objective of the equipment condition indicators that correlate to the ability of equipment to perform its safety function. The VRLA Battery Cell Assembly was aged by 10 years (minimum) in accordance with conditions specified by Bechtel Equipment Qualification Datasheet 24590-LAW-EUQ-UPE 00003 Rev. 3 and the process conditions specified by the Instrument Data Sheet 24590-LAW EUD-UPE-00009 Rev. 2. Greenberry Industrial has contracted with GNB Industrial Battery Co located at 4115 S Zero St, Fort Smith, AR 72908 to perform age conditioning, monitoring, and capacity testing in accordance with Environmental Qualification Plan 550001.001-35.0.1 Rev. 1. The required process at the stated conditions set by the parameters established by the plan were completed satisfactorily. The details of the of the test process observed by Greenberry is detailed in the attached Seismic Test Log 550001.001-7.0.3, including examples of the objective evidence collected during the qualification process.
54 ENVIRONMENTAL SCIENCES↗
The importance of cycle-by-cycle data in performing rapid battery technology development and validation
Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.
25 ENERGY STORAGE↗
Update on systematic cycle aging of NMC and NCA 18650 Li-ion batteries.
Abstract not provided.
Wittman, Reed↗
Machine-Learning Assisted Identification of Battery Life Models
Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.
battery↗
Rapid Electrochemical Diagnosis of Battery Health and Safety from Cells to Modules
Rapid electrochemical diagnosis of battery health and failure is critical for ensuring reliable battery performance and battery safety. Traditional battery health diagnostics such as capacity measurements and DC pulse tests are reliable and well-understood, however, these measurements of battery capacity and resistance do not capture all aspects of battery degradation. Other aspects of degradation, such as electrolyte decomposition, lithium-plating, and particle cracking are difficult to detect electrochemically but are crucial to measure to get a full picture of battery safety and flag out potential failures. In this work, lab- and field-aged commercial lithium-ion batteries and modules of various chemistries and formats are tested using a variety of traditional electrochemical characterization methods as well as using 2-minute pseudo-random DC pulse sequences at rest and during charge/discharge. The electrochemical measurements are compared to physical cell measurements, cell efficiency, drive cycle performance, physical and thermal heterogeneity, and qualitative safety metrics using statistical and machine-learning methods to discover if a comprehensive "battery health map" can be accurately identified using only rapid DC measurements.
ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
BLAST-Lite (Battery Lifetime Analysis and Simulation Tool - Lite) [SWR-22-69] Related to: BLAST aka: BLAST-Py
Battery Lifetime Analysis and Simulation Toolsuite (BLAST) provides a library of battery lifetime and degradation models for various commercial lithium-ion batteries from recent years. Degradation models are identified from publicly available lab-based aging data using NREL's battery life model identification toolkit. The battery life models predicted the expected lifetime of batteries used in mobile or stationary applications as functions of their temperature and use (state-of-charge, depth-of-discharge, and charge/discharge rates). Model implementation is in both Python and MATLAB programming languages. The MATLAB code also provides example applications (stationary storage and EV), climate data, and simple thermal management options. For more information on battery health diagnostics, prediction, and optimization, see NREL's Battery Lifespan webpage.
Smith, Kandler↗
Chapter 8: Life-Cycle Testing and Analysis
Prior to a spacecraft launch, program mission assurance standards dictate that the flight battery power system should comply with mission requirements under the intended operating conditions. Ground life cycle testing (LCT) combined with an analysis on the electrical power system (EPS) battery is an empirical method used to demonstrate compliance to satellite service life requirements. The LCT compliance method adopted by the aerospace industry is based on demonstrating a space battery's life expectancy as a part of battery qualification. Real-time cell and lithium-ion battery (LIB) LCT data are commonly used for model inputs to EPS power and energy balance analyses, in LIB reliability analysis estimates, and to support on-orbit spacecraft mission life extension opportunities. This chapter describes the LCT planning steps, process approach, and analysis techniques commonly used to qualify space LIB power systems.
accelerated aging↗
Role of Coatings as Artificial Solid Electrolyte Interphases on Lithium Metal Self-Discharge
Artificial solid electrolyte interphases have provided a path to improved cycle life for high energy density, next-generation anodes like lithium metal. Although long cycle life is necessary for widespread implementation, understanding and mitigating the effects of aging and self-discharge are also required. In this report we investigate several coating materials and their role in calendar life aging of lithium. We find that the oxide coatings are electronically passivating whereas the LiF coating slows charge transfer kinetics. Furthermore, the Coulombic loss during self-discharge measurements improves with the oxide layers and worsens with the LiF layer. It is found that none of the coatings create a continuous conformal, electronically passivating layer on top of the deposited lithium nor are they likely to distribute evenly through a porous deposit, suggesting that none of the materials are acting as an artificial solid electrolyte interphase. Instead, they likely alter performance through modulating lithium nucleation and growth.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Interfacial Pressure Improves Calendar Aging of Lithium Metal Anodes
Lithium metal is a very attractive anode material because its theoretical specific capacity is approximately 10 times higher than conventional graphite anodes. Despite great promise, Li anodes suffer from capacity fade due to instabilities with the electrolyte as well as stranding of active Li. We have previously shown that applied interfacial pressure improves Li anode cycling because the pressure reduces the propensity for Li isolation and enables easier reconnection. Many researchers have also shown that calendar aging can lead to Li capacity loss and this has been attributed to either electrolyte decomposition with concurrent Li corrosion or to the formation of stranded Li. Our prior research focused on calendar aging during cycling suggests the mechanism for calendar aging is largely related to stranding of Li during rest and reconnection of the stranded Li upon further cycling, evidenced by similar average Coulombic efficiencies and Li loss in cells with and without rest. Because our calendar aging studies suggest Li stranding as a major cause of Coulombic efficiency drops and our Li cycling studies suggest this can be mitigated partially through applied interfacial pressure, we hypothesized that applied pressure would improve calendar aging by reducing stranded Li and enabling reconnection. We systematically varied applied pressure (0-1000 kPa) on Li metal anodes during cycling tests with and without intermittent calendar aging periods. Though the Coulombic efficiency decreases during aging periods, the lost capacity is recovered during subsequent cycles, as shown though average Coulombic efficiency and cumulative Li capacity loss analysis. We find that application of pressure partially mitigates calendar aging, in accordance with our hypothesis that calendar aging is caused by Li standing and can be mitigated to some degree with interfacial pressure. This is further supported by our results showing that the average Coulombic efficiency and cumulative Li capacity losses are similar over 50 cycles for cells that were continuously cycled and cells with periodic calendar aging periods. This result indicates that the losses during aging are reversible, which is consistent with Li stranding and reconnection. We show that pressure is one mitigation technique that helps reduce Li calendar aging in this study, but our finding that calendar aging is primarily governed by the stranding and reconnection of dead Li has wider implications. This research suggests that other mitigations which have been shown to prevent dead Li formation or encourage reconnection during cycling would also likely be successful for the purpose of improving calendar aging. The authors were supported by a Laboratory Directed Research and Development (LDRD) program. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC (NTESS), a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration (DOE/NNSA) under contract DE-NA0003525. This written work is authored by an employee of NTESS. The employee, not NTESS, owns the right, title and interest in and to the written work and is responsible for its contents. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Any subjective views or opinions that might be expressed in the written work do not necessarily represent the views of the U.S. Government.
applied pressure↗
Rational Electrolyte Design for Elevated-Temperature and Thermally Stable Lithium-Ion Batteries with Nickel-Rich Cathodes
As the energy density of lithium-ion batteries (LIBs) increases, the shortened cycle life and the increased safety hazard of LIBs are drawing increasing concerns. To address such challenges, a series of localized high-concentration electrolytes (LHCEs) based on a solvating-solvent mixture of tetramethylene sulfone and trimethyl phosphate and a high flash-point diluent 1H,1H,5H-octafluoropentyl 1,1,2,2-tetrafluoroethyl ether were designed. The LHCEs exhibited non-flammability and greatly suppressed heat release at high temperatures, which would potentially improve the safety performance of the LIBs. Moreover, the optimal LHCE achieved capacity retentions of 87.1% and 81.7% in graphite||LiNi 0.8 Mn 0.1 Co 0.1 O 2 cells after 500 cycles at 25 °C and 45 °C, respectively, which were significantly better than the conventional electrolyte, whose capacity retentions were only 75.2% and 38.5% under the same condition. Mechanistic studies revealed that the LHCE not only formed a more robust solid electrolyte interphase, but also exhibited improved anodic stability, compared with the conventional electrolyte. Further, this work sheds light in rational electrolyte design for high energy density LIBs with high battery performance and low safety concerns.
25 ENERGY STORAGE↗
Capacity and Coulombic Efficiency Measurements Underestimate the Rate of SEI Growth in Silicon Anodes
Capacity measurements and related quantities are the first layer of information acquired during testing of Li-ion cells. It is generally considered that elevated values of coulombic efficiency and capacity retention are absolute indicators of the existence of a stable solid electrolyte interphase (SEI). Here, we challenge this notion by analyzing how the effect of side reactions on cell capacity depends on the choice of electrodes. More specifically, we demonstrate that the extent of measurable capacity fade due to SEI growth is modulated by the shape of the voltage profile of the cathode and anode at the end of charge and discharge half-cycles. This shape-dependency creates a mismatch between SEI growth and cell capacity loss, which is relatively small for graphite anodes but sizable for silicon-containing electrodes. We illustrate this point by showing that, at the same coulombic efficiency and capacity retention, cells containing silicon-based materials could actually exhibit rates of SEI growth that are as much as ≥ 40% higher than graphite cells. The main implication of this behavior is that, for certain systems, capacity measurements may be an unreliable source of information about the extent of reactions at the SEI, allowing other consequences of these side reactions (such as electrolyte depletion) to proceed unchecked while the cell appears to be stable.
25 ENERGY STORAGE↗
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.
25 ENERGY STORAGE↗
Stoichiometric irreversibility of aged garnet electrolytes
Solid-state lithium batteries (SSLBs) have been regarded as one of the next-generation energy storage systems. With the adoption of solid-state electrolytes (SSEs) and lithium metal anodes, SSLBs enable higher energy density and more reliable safety than the state-of-the-art lithium-ion batteries. Among potential SSEs, the cation-doped Li 7 La 3 Zr 2 O 12 (LLZO) is promising for its high ionic conductivity (~10 –3 S cm –2 ) at room temperature and high stability with Li metal anode. However, the storage of doped LLZO in the ambient condition suffers the aging effect, including the structural transition (i.e. low-temperature cubic form) and the stoichiometric changes (i.e. Li 2 CO 3 ). These changes are detrimental to LLZO ionic conductivity and interfacial stability in SSLBs. To this end, in this study we are motivated to investigate the structural and stoichiometric reversibility of aged LLZO during thermal treatment. With the help of an in-situ synchrotron-based high-energy X-ray diffraction technique, our experiments revealed that the LLZO powders became a low-temperature cubic phase when exposed to the ambient condition for an extended period of time. A high temperature cubic form can be restored after a thermal treatment of the aged LLZO powder, regardless of the type of dopant. However, the restoration of the stoichiometry remained a challenge, and the degree of the restoration showed a clear dependence on the dopant chemistry.
36 MATERIALS SCIENCE↗