UL Research Institutes

Increasingly complex and heterogeneous chemical reactions on the battery electrode surfaces require the characterization methods to provide a complete picture of localized and dynamic molecular interactions and Li diffusion information. In this presentation, we introduce our efforts in developing and utilizing operando optical microscopy to study the dynamic processes in solid-state battery reactions. Using a reflection-mode optical microscope, we imaged localized lithium diffusion within NMC particles, revealing significant particle size-dependent phenomena. Larger NMC particles exhibited pronounced diffusion delays at different locations, whereas smaller particles demonstrated a much faster diffusion process. Additionally, we extracted the lithium diffusion coefficients of individual NMC particles, identifying a clear dependence on particle size. The study further explores lithium diffusion under varying charging rates and temperatures. Complementary to these findings, operando Raman microscopy was employed to characterize localized chemical compositions during solid-state battery reactions, providing deeper insights into reaction mechanisms.

Recent Battery Energy Storage System (BESS) failures highlight the need to detect gases from venting cells as quickly as possible, as well as the vulnerabilities in existing monitoring infrastructure that would alert the occurrence and location of cell venting [1]. Conventional detection strategies, which rely on stationary gas sensors, can fail to identify low-volume gas release from single-cell early stage (first) venting, especially in large-scale BESS that have multiple racks and modules, such as Panel A in Figure 1. Suboptimal or improper fixed sensor placement, gas transport effects (e.g., thermal buoyancy), and dilution below detection thresholds further complicate failure detection. These limitations hinder emergency responders’ ability to accurately assess explosion and/or toxicity risks.To address these challenges, we propose integrating a guided mobile gas sensing platform to complement stationary gas sensors. This system will dynamically monitor spatial and temporal gas evolution during early failure stages by deploying a robotic platform equipped with commercial sensors to detect the most commonly emitted gases (CO2, H2, CO, electrolyte vapor). The robot will patrol accessible areas or be strategically guided to areas of interest to actively sample “suspicious zones” with high concentrations of vent gases. This target navigation will be informed by advanced state-of-health (SOH) diagnostics derived from electrical and thermal measurements typically available in the Battery Management System (BMS) data.A major component of our detection algorithm is to recognize how rapidly a fault is propagating to other cells or the time elapsed from the vent initiation. To this end, we first characterize venting gases in terms of composition, quantity, temperature, and time evolution. A literature review (Figure 1, Panel B) on gas characterization was conducted, and it was found that most prior work focused on global, post-thermal runaway (TR) gas analysis. However, the combustion of vent gases during TR makes their characterization challenging, leaving first venting behavior poorly understood. To address this gap, we aim to develop a systematic way to trigger gas generation without full thermal runaway by inducing controlled overheating and arresting the heat after first venting occurs. We will apply this method to two form factors: cylindrical (2.6 Ah) and prismatic (32 Ah), focusing on LFP cells due to their widespread adoption in grid-scale BESS.After characterizing single-cell behavior, we will construct a representative BESS rack (Figure 1, Panel C) with cells inside modules to study venting propagation from vented cells to the module venting channels and to the rack headspace where the stationary sensors are typically located. Our gas characterization will then inform models and algorithms that can recognize the various stages of an evolving failure, including the location and the number of cells undergoing slow discharge, venting, and/or the propagation of thermal runaway. This will guide the robotic sensor platform to reach the appropriate venting channel, providing a faster response and better guidance for first responders.By integrating robotic mobility with diagnostic and prognostic tools, this will not only improve the detection of first venting events, but also enhance situational awareness for emergency response. It enables high-confidence localization of first venting events, estimates the scale of the fault progression, and quantifies the number of cells involved in or propagating in a thermal runaway event, providing actionable data to guide safer and more rapid containment efforts.ACKNOWLEDGEMENTThis project is supported by UL Research Institutes.REFERENCES[1] Srinivasan, L., Shaw, S. and Billaut, E. (2024). Insights from EPRI’s battery energy storage systems (BESS) failure incident database: analysis of failure root causeFigure 1: A typical large-scale Battery Energy Storage System (BESS) with multiple racks and modules (Panel A), an overview of literature showing the lack of vent (pre-thermal runaway) gas characterization for LFP cells (Panel B), and a representative BESS rack with a mockup of the mobile guided gas sensing platform directed to sample from a module vent (Panel C). Figure 1

A battery is a collection of electrically connected cells and a smart circuit board that provides the relevant voltage and capacity and subsequently the energy, and/or power. These electrical connections are vital to the performance and safe operation of the battery. Single lithium-ion cells provide nominal voltages that can range from 3.8 V to 2.4 V (titanate anodes) with the highest charge voltage being 4.35 V (for NMC cathodes) today. In order to manufacture a battery that requires voltages higher than the single cell voltages, one needs to assemble them in various series/parallel combinations. The battery design can be configured with cells in parallel first forming a bank and then arranging the banks in series (referred to as PS configuration) or in series first forming a string and then arranging the strings in parallel (referred to as SP configuration). A third design exists which is called the matrix design where cells are in parallel and then the banks are arranged in series but the cells in each bank are also connected electrically to the neighboring bank in series string. Past studies on off-nominal conditions such as overdischarge has indicated that matrix configurations prevent imbalanced voltages as well as cell voltages to go below 0 V. However, the first step in this study will characterize the performance of these various design configurations.Cycle life testing on small modules with these different configurations have been carried out to understand how the differences in electrical connections affect the life of the modules. In parallel, data from the cycle life testing has been utilized to develop a coupled electric-electrochemical-thermal model to understand the effects of connectivity on the current, voltage and temperature of the cells in the module that then may lead to performance changes.

Windows are a potential vulnerability of structures during exterior fire exposures, such as those from wildland-urban interface (WUI) fires or a neighboring structure fire in a residential community. Window failure creates a pathway for embers, flames, and hot gases to enter a building and ignite interior combustibles. There is a need to investigate the failure of different types of multi-pane windows during exposures similar to those incident on buildings immediately adjacent to a burning structure. Eight experiments were performed with 16 window pane assemblies mounted in a target facade placed in front of a compartment fire that transitioned through flashover. Double pane window assemblies with both panes plain (annealed) glass, both panes tempered glass, and one plain glass, one tempered glass pane were examined. Window pane assemblies with a fire side plain glass pane and back side tempered glass pane performed notably better than assemblies with the opposite orientation. The heat load (heat flux integrated over time) at the time of complete failure (both panes cracked) was typically between 2.5 MJ/m2 and 10.0 MJ/m2 for window pane assemblies with a plain glass back side pane and between 9.0 MJ/m2 and 17.5 MJ/m2 for those with a tempered glass back side pane. Total heat flux measured behind the pane assemblies exceeded critical values for non-piloted ignition of common household materials, even before complete failure. Results from this study can be used to inform building codes and homeowner guidance related to the installation of tempered pane windows in areas prone to WUI fire hazards.

Organic matter (OM) is a major contributor to the oxidative potential (OP) of indoor PM2.5. Portable air purifiers equipped with high-efficiency particulate air (HEPA) filters are known to effectively remove indoor PM2.5 mass concentrations. However, their impacts on the OP and sources of indoor PM2.5 OM and whether these impacts could further affect the OP of indoor PM2.5 metals remain poorly understood. In a crossover trial, each of the 43 asthmatic children underwent a 2-week true filtration (HEPA + activated carbon [AC]) and sham filtration (no HEPA + no AC), with randomized order. PM2.5 samples, simultaneously collected in participants’ bedrooms and outside their homes, were measured for mass concentration, composition, mass-normalized OP (OPm, OP per mass), and volume-normalized OP (OPv, OPm × mass concentration). Compared to the sham filtration, indoor PM2.5 OM mass was 34% lower, OPm was 70% lower, and OPv was 80% lower during true filtration. The reduction in OM OPv was largely attributed to removing more reactive outdoor OM and indoor secondary OM. The change in OM composition also contributed to the reduced PM2.5 metals’ OPm. Our results suggest that indoor air purifiers with HEPA and AC filters efficiently reduce PM2.5 OPv by removing OM.

Fire and explosion hazards present a serious concern to the widespread adoption of battery technology. This work experimentally investigates the explosion hazards associated with synthesized lithium-ion battery thermal runaway effluent gases (TREG) in an enclosed garage space typical of modern construction in North America. Pressure rise inside the compartment is examined using high-frequency piezoelectric pressure transducers. Data on overpressure and impulse is compared with known ranges for structural damage and bodily injury thresholds and calculation methods for maximum overpressure. Data on time-resolved overpressure are compared with a vented explosion model from literature. These comparisons support that existing models for maximum overpressure and time-resolved pressure rise remain valid for the examined synthesized TREG. Correlations are developed between gas volume and measured impulse and overpressure. An extension of this analysis with values of TREG production from literature supports that pressures measured in these experiments can be generated from ignition of gases released from batteries sized in the range of 3300W⋅h to 80 500W⋅h.

Providing NFPA 1403 compliant live-fire training can present thermal and chemical exposure risk to instructors and students. To reduce risk, training academies, fire departments, instructors, and standards setting technical committees need more information on how different training fuels used in common training structures can impact the environment in which firefighter training occurs. This study utilized a traditional concrete training structure with multiple compartments to characterize training environments with three different fuel package materials [i.e., low density wood fiberboard, oriented strand board (OSB), and wood pallets]. Exposure risks for a fire instructor located on either the first or second floor were characterized using measurements of heat flux, air temperature and airborne concentrations of several contaminants including known, probable, or possible carcinogens. It was hypothesized that utilizing a training fuel package with solid wood pallets would result in lower concentrations of these airborne contaminants [aldehydes, polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs)] than wood-based sheet goods containing additional resins and/or waxes. Additionally, it was hypothesized that these concentrations would be lower than in the single compartment Fire Behavior Lab presented in a companion manuscript. For all measured compounds other than hydrochloric acid, airborne concentrations were 10 to 100 times lower than in the Fire Behavior Lab. OSB-fueled fires produced the highest median concentrations of total PAHs and VOCs such as benzene, while the pallet fuel package produced the lowest median concentrations of these compounds. These trends generally followed the qualitative visual obscuration created by each fuel. Additional tests were conducted on the OSB-fueled fires with increased ventilation and an alternate means of reducing visibility through smoldering smoke barrels. This OSB experiment with increased ventilation resulted in the highest temperatures in the fire room but the lowest impact on visibility throughout the structure, as well as the lowest overall concentrations of contaminants in this study. In contrast, the smoldering straw-filled smoke barrel created a highly obscured environment (with minimal impact on thermal environment) and some of the highest concentrations of the targeted contaminants of any test. These data may be useful in balancing obscuration for training with potential exposure to thermal stressors and contaminants.

Computational fire models are a valuable tool for engineers, investigators, and researchers, but the governing equations and solution approaches typically are too complicated, and the models rely on too many variables to allow for direct uncertainty analysis and error propagation. Without these necessary steps in hypothesis testing and model validation, the true accuracy of predictions cannot be completely quantified. Additionally, there is no standard accepted methodology to determine the kinetics of thermal degradation of a material, which can further complicate uncertainty quantification, validation, and interlaboratory studies. Recently, the polynomial chaos expansion method has emerged as a means to conduct uncertainty quantification on complicated models with relatively low computational cost. A method is presented in this work to determine the kinetics of pyrolysis for polycarbonate and to quantify the uncertainty in the mass loss rate prediction. Using this methodology, predictions of mass loss rate measured in themogravimetric experiments showed relative standard deviations of the peak value ranging from 16% to 23% for heating rates ranging from 30 to 3 K/min. The experimental data were within the bounds of uncertainty of the mass loss rate predictions. This study demonstrated a successful parameterization methodology and validated the use of generalized polynomial chaos to inexpensively quantify uncertainty in pyrolysis model predictions.

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTA Research Agenda for the Chemistry of Fires at the Wildland–Urban Interface: A National Academies Consensus ReportMegan E. Harries*Megan E. HarriesNational Academies of Sciences, Engineering, and Medicine, Washington, District of Columbia 20001, United States*Email: [email protected]More by Megan E. HarriesView Biographyhttps://orcid.org/0000-0001-5212-1697, David T. Allen*David T. AllenUniversity of Texas at Austin, Austin, Texas 78712, United States*Email: [email protected]More by David T. AllenView Biographyhttps://orcid.org/0000-0001-6646-8755, Olorunfemi AdetonaOlorunfemi AdetonaDivision of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, Ohio 43210, United StatesMore by Olorunfemi Adetona, Michelle L. BellMichelle L. BellYale University, School of the Environment, New Haven, Connecticut 06511, United StatesMore by Michelle L. Bell, Marilyn S. BlackMarilyn S. BlackChemical Insights Research Institute, Underwriters Laboratories Inc., Marietta, Georgia 30067, United StatesMore by Marilyn S. Black, Jefferey L. BurgessJefferey L. BurgessMel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85724, United StatesMore by Jefferey L. Burgess, Frederick L. DryerFrederick L. DryerDepartment of Mechanical Engineering, University of South Carolina, Columbia, South Carolina 29208, United StatesMore by Frederick L. Dryer, Amara L. HolderAmara L. HolderOffice of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United StatesMore by Amara L. Holderhttps://orcid.org/0000-0001-6443-5298, Ana MascareñasAna MascareñasIndependent Consultant, Washington, District of Columbia 20010, United StatesMore by Ana Mascareñas, Fernando L. Rosario-OrtizFernando L. Rosario-OrtizDepartment of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United StatesEnvironmental Engineering Program, University of Colorado Boulder, Boulder, Colorado 80309, United StatesMore by Fernando L. Rosario-Ortizhttps://orcid.org/0000-0002-3311-9089, Anna A. StecAnna A. StecCentre for Fire and Hazards Sciences, University of Central Lancashire, Preston, PR1 2HE, United KingdomMore by Anna A. Stec, Barbara J. TurpinBarbara J. TurpinDepartment of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United StatesMore by Barbara J. Turpin, and Judith T. ZelikoffJudith T. ZelikoffDivision of Environmental Medicine, NYU Grossman School of Medicine, New York, New York 10010, United StatesMore by Judith T. ZelikoffCite this: Environ. Sci. Technol. 2022, 56, 22, 15189–15191Publication Date (Web):October 26, 2022Publication History Received24 September 2022Published online26 October 2022Published inissue 15 November 2022https://doi.org/10.1021/acs.est.2c07015Copyright © 2022 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY-NC-ND 4.0. License Summary*You are free to share (copy and redistribute) this article in any medium or format within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. This publication is Open Access under the license indicated. Learn MoreArticle Views1510Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (3 MB) Get e-Alertsclose Get e-Alerts