bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pulse Voltammetry Analysis of Li-Ion Battery Degradation
Authors: Kabra Venkatesh , Fear Conner , Northrop Paul , Cole James , Mukherjee Partha P. 
Published: July 2021

Pulse voltammetry is a class of electroanalytical techniques which involve applying voltage pulses to understand the redox reaction in an electrochemical system. The advantages of such a technique is high sensitivity of pulse voltammograms towards kinetic and thermodynamic parameters. Here, a physics-based model is developed to simulate the Li-ion battery physics and the resultant pulse voltammogram for generic voltage pulses. We develop an in-situ diagnostic tool by combining it with a parameter estimation technique to accurately, rapidly and quantitatively capture the key parameters. A combined experimental and computational-modelling approach allows us to parametrize the state of Li-ion battery system. Degradation reactions such as solid electrolyte interphase growth, active material slippage, and loss have been considered to capture the pertinent degradation modes responsible for capacity fade under different experimental conditions. This method can be extended to integrate with battery management systems for state of health estimation and monitoring.
The objective of this effort is to quantitatively characterize battery properties in order to develop accurate physics-based models suitable for model-based development of fast charging protocols and accurate state-of-charge and state-of-health prediction. Pulse voltammetry analysis software and battery electrode characterization approaches provide the ability to determine quantitative values, with known confidence intervals, for input parameters of physics-based models describing battery performance. Pulse voltammetry characterization, in conjunction with physical analysis and complementary techniques such as galvanostatic intermittent titration (GITT) and electrochemical impedance spectroscopy (EIS) will be utilized to analyze the battery state. These quantitative methods enable the development and parameterization of predictive models for both battery performance and undesired processes, such as lithium plating, that limit charging rates and cycle life of lithium-ion batteries.
This presentation will focus on DPV data gathered periodically during cell cycling to identify the progression of capacity loss and degradation. The DPV analysis approach, which utilizes the experimental data to estimate kinetic and thermodynamic parameters of the model for battery performance, will be presented. The experimental characterization results will include example DPV measurements as a function of state-of-charge, cycling history, and operational temperature. Examples of data analysis to estimate model parameters with statistical confidence measures, and interpretation of the change in voltammogram response with cycling will be discussed.

Figure 1. Fitting model results with experimental differential pulse voltammetry data for parameterization

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Simulation and Validation Testing of the Effect of Novel Electrolyte Design on Thermal Propagation of Lithium-Ion Batteries
Authors: Torres Gabriel , Raghunathan Aditya , Vaidya Rutvik , Moganty Surya 
Published: July 2021

Lithium-ion batteries are an increasingly prevalent source of power in grid storage applications and electric powertrains, given its high energy density and extended cycle life. However, lithium-ion batteries carry significant safety risks and are a growing hazard due to possible fires that can be caused by overheating, physical cues (i.e., crushing or penetration of cell), overcharging and short-circuits. These risks are greatly increased when focus shifts from a single cell to either modules or packs, where an individual cell entering thermal runaway propagates to adjacent cells. In spite of multiple protections battery packs have in recent times been known to fail.
It is well understood that thermal runaway occurs when the self-heating rate in the cell exceeds the heat dissipation out of the cell. This self-heating is the product of undesired side-reactions in the cell which are onset by abuse conditions such as excess heat, internal short circuits, etc. The key to inhibiting thermal runaway is to modify the cell such that the self-heating rate under abuse conditions is significantly reduced. Reducing the self-heating rate is important when considering battery packs that consist of many cells as is the case in electric vehicles, so that if one cell fails in a pack the thermal runaway does not propagate to adjacent cells. Our approach therefore is to modify the electrolyte system to reduce the overall cell self-heating rate. This is a tunable, drop-in solution to addressing the need for improving battery safety.
The incorporation of NOHMs novel safe electrolyte formulation is shown to impact the abuse tolerance of cells on both the material level, i.e. the electrolyte-electrode interaction and on the cell level. The presented electrolyte systems demonstrate reduced overall energy and peak heat flow in the exotherm profiles of NMC cathode materials as well as lithiated graphite via differential scanning calorimetry (DSC). With this approach, we demonstrate significantly lower self-heating rates in an 18650 lithium-ion cell via an accelerating rate calorimeter (ARC).
Using the self-heating rate data produced via ARC testing we developed a thermal runaway model to simulate thermal propagation in battery packs consisting of seven 18650 lithium-ion cells arranged in a honeycomb configuration. The self-heating rate is translated into thermal power generation for individual cells surrounding a ‘trigger cell’, a cell that has entered thermal runaway. The model suggests that changes in the electrolyte system as measured by ARC mitigates cell-to-cell thermal propagation in event of an individual cell thermal runaway (Figure 1). This simulation is then validated with a real pack by inducing thermal runaway in the center cell with a nail penetration test. NOHMs novel safe electrolyte development carries significance in not only safer individual cells but also in safer overall battery packs.

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Detection of First Venting in Battery Failures with Carbon Dioxide Sensors
Authors: Cai Ting , Tran Vivian , Engle Brian , Stefanopoulou Anna , Siegel Jason 
Published: July 2021

Li-ion battery thermal runaway is a critical safety issue for large-scale battery packs. Upon battery failure, gas venting can occur when the cell's internal pressure exceeds a critical value and causes cell rupture and electrolyte leakage. The gas venting is usually categorized into first venting events and thermal runaway gas venting events (second venting). The first venting occurs around 120 °C and can be considered as a precursor to thermal runaway. The subsequent venting during thermal runaway is more aggressive and releases more vent-gas.
Detecting battery failures immediately after the first venting is critical to take action before the failure evolves into a thermal runaway event. Therefore, a gas detection method targeting first venting gas will be the focus. Early studies of first venting gas composition showed that CO2 is the primary gas component in battery overheating tests, nail penetration tests, and overcharging tests, while the gas composition in battery external short circuit tests was not revealed.
To this end, an external short circuit test for a pouch 4.6 Ah cell was performed in a non-sealed acrylic box, which has around 21.5 L volume. Shortly after the initiation of external short circuit, the battery expansion force rises due to gas generation inside the pouch cell. The force then dropped suddenly indicating a gas venting event, which occurred when the cell temperature reached 114 °C. A Non-Dispersive Infrared (NDIR) CO2 sensor was used. The CO2 sensor reading started to increase within 5 seconds of gas venting and reached a maximum of 1,500 ppm as shown in the figure.
To apply the gas detection strategies for first venting events, a detection threshold is required for different cells at different abuse conditions. To model the gas evolution process, the evolution of side reactions, mainly the SEI decomposition at high temperatures was considered. The released CO2 in first venting events can be estimated at different abuse conditions. The estimated CO2 amount can help determine the detection threshold of gas concentration for a given battery system.

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The Effect of Internal Pressure on Thermal Runaway Temperature
Authors: Lian Torleif , Troøyen Susanne Hansen , Forseth Sissel 
Published: July 2021

Lithium-ion batteries have developed into one of the most popular secondary batteries on the market today due to high voltage, long lifetime and high energy density. However, lithium ion batteries may have safety issues, and several fires are reported. Thermal stability is one of many parameters used to evaluate the safety aspects of lithium ion batteries. Understanding thermal stability at material level is essential for the further development into safer lithium ion batteries.
Calorimetric methods are widely used in thermal stability tests for lithium-ion battery components. Accelerating rate calorimetry (ARC) is one of the most popular methods used to analyse full cells and differential scanning calorimetry (DSC) is the most common method for testing thermal stability of materials. DSC is however less sensitive than ARC, and uses smaller sample sizes in a fixed volume. This makes comparison between material samples and full cells difficult. Currently, there are no standard procedures for ARC tests on battery materials, and this work is an effort to standardize ARC lithium-ion material measurements.
The thermal stability of LiNi0.40Mn0.37Co0.23O2 fully charged cathode material from a commercial 8 Ah pouch cell with 1 M LiPF6 in EC/DMC (1:1) was studied by ARC. Four different calorimetry test setups were tested in order to understand the influences of the setup on the result. Regardless of the setup, the cathode/electrolyte mixture was found to have two stages of self-heating, where the rate of the first stage influenced the temperature at which thermal runaway (heating rate > 10°C min-1) occurred during the second stage. During the initial stage of self-heating (175–240°C), the setup was not found to impact the results. At temperatures above 240°C, the reactivity was found to be highly dependent on pressure. Samples at low pressure (near 1 bar) did not reach thermal runaway, whereas the samples in the higher-pressure setups all reached thermal runaway at 250–260°C. Figure 1 shows the ARC measurements of two NMC cathode materials mixed with electrolyte at approximately 1.2 bar and 3.9 bar at 315 °C.
It was concluded that the test setup is very important for the outcome of thermal stability measurements on fully charged LiNi0.40Mn0.37Co0.23O2 cathode material, and the reactivity of this cathode material is dependent on pressure. This is an important finding because most lithium-ion cells will have ventilated at temperatures above 150–200 °C and experience reduced internal pressure. DSC and ARC material measurements in closed containers with high pressure would not be representative for measuring lithium-ion cathode material reactivity.

Figure 1: Heat rates and pressure for two samples of LiNi0.40Mn0.37Co0.23O2 with 1 M LiPF6 in EC/DMC (1:1) in two different setups.

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Li-Ion Battery Deactivation through External Short Circuit
Authors: Tran Vivian , Cai Ting , Stefanopoulou Anna , Siegel Jason 
Published: July 2021

While electric vehicle battery packs are designed to address safety issues, battery failures can still occur. Most prominently, electric vehicle fires can occur after accidents or from internal shorts due to defects, abuse, or accelerated aging. Battery fires can be difficult to manage due to the active lithium materials and the presence of both the fuel and oxidant produced by thermal decomposition. Typically, after extinguishing the original fire, the main priority is to cool the battery pack by applying a large and continuous volume of water directly to the pack. However, the fire may still reignite hours later, fueled by the remaining stored energy. This highlights the need for further investigation and discussion about how to effectively remove the stored energy from, or deactivate, batteries with the goal to prevent fire re-ignition in battery packs.
Batteries at a lower state-of-charge (SOC) are more thermally stable and have a lower energy release during thermal runaway than those at high SOCs. In addition, experimental results from single cells and small, multiple-cell configurations undergoing external short circuits show that thermal runaway (TR) events are rare, and the cell peak temperatures depend on the relative size of the external resistance to the internal cell resistance. To this end, this research aims to evaluate the use of high C-rate discharge and external short circuits to safely deactivate batteries while avoiding TR.
External short circuit testing was performed on 4.6Ah pouch cells at 15% and 100% SOC, with measurements of cell current, voltage, temperature, expansion force, and atmospheric CO2 gas concentration. At the end of the test, the cell initially at 15% SOC discharged to -10% SOC and reached a maximum temperature of 64℃. The cell had no visible damage afterwards. For the 100% SOC cell, it reached a maximum temperature of 120℃ and discharged to around 25% SOC at the end. In both cases, neither went into thermal runaway, but the first cell was able to discharge to less than 0% SOC in 5 minutes, while the 100%SOC cell took significantly longer to reach the low current cutoff. This is because during the gas venting process, electrolyte is lost, greatly increasing the internal resistance and increasing the discharge time. In addition, the lower peak temperature of the first cell indicates a reduced likelihood of thermal runaway propagation in a pack, and the lack of toxic and flammable vent gas and electrolyte leakage in the environment improves safety. In order to apply this at a larger level, we will investigate the appropriate external resistance to safely deactivate the cells without leading to hazardous conditions.

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Investigation of Li Metal Plating and Dissolution on Graphite Electrodes
Authors: Hein Simon , Hogrefe Christin , Waldmann Thomas , Danner Timo , Richter Karsten , Wohlfahrt-Mehrens Margret , Latz Arnulf 
Published: July 2021

Li plating, meaning the deposition of metallic Lithium on the negative electrode is considered as one of the major degradation mechanisms in Li-Ion batteries. Therefore, understanding the processes which occur between metallic Lithium and state-of-the-art electrode materials, mainly Graphite, is important to improve the life-time of Li ion cells.
Lithium plating and stripping is explored using an electrochemical model, which explicitly includes the growth of plated lithium as a competing process to the direct intercalation lithium ions [1]. The model is combined with a thermodynamically consistent transport theory and implemented in the simulation framework Battery and Electrochemistry Simulation Tool (BEST). The extended framework is used to conduct a first study regarding the impact of an inhomogeneous SEI growth on the deposition of metallic lithium.
Moreover, we will present recent work on simulations of model experiments investigating the dissolution of lithium deposits on Graphite electrodes [2]. Different electrolyte formulations and configurations of a lithium metal disk on the electrode are investigated to evaluate different lithium transport pathways. In each experiment some pathways are intentionally blocked, thus yielding interesting insights into the dissolution mechanism. Post-mortem analysis on the Graphite electrodes is performed using Raman spectroscopy and glow-discharge optical emission spectroscopy (GD-OES) revealing the spatial distribution of the lithium at the end of the dissolution experiment. At the same time measurements of the open circuit voltage are performed to monitor the progress of the dissolution process. Our simulations are in very good qualitative agreement with measured OCV curves and the observed lithium distributions.
Therefore, the simulations provide additional “in-operando” insights to the plating as well as the dissolution of a metallic lithium phase. With the mechanistic insights optimization of the electrode and cell architecture can be deduced which help to improve the life-time of Li ion cells.
[1] S. Hein, T. Danner, and A. Latz, “An electrochemical model of lithium plating and stripping in lithium-ion batteries,” ACS Appl. Energy Mater., vol. 3, no. 9, p. acsaem.0c01155, Jul. 2020.
[2] C. Hogrefe et al., “Mechanistic Details of the spontaneous Intercalation of Li Metal into Graphite Electrodes,” J. Electrochem. Soc., Nov. 2020.
Machine Learning for the Quantification and Identification of Natural Gas from Mixed Potential Electrochemical Sensors
Authors: Tsui Lok-kun , Halley Sleight , Agi Kamil , Garzon Fernando H 
Published: July 2021

Methane emissions from natural gas pipeline infrastructure contribute to over 2 billion dollars in losses in the US and 1.6 x 108 metric tons of CO2-equivalent per year.[1-2] Widely deployable sensors are needed to quickly identify and repair leaks so that their environmental and economic impact can be minimized. Mixed potential electrochemical sensors (MPES) are a low cost, highly robust system which can be deployed in the field. Mixture identification and quantification using sensor signals produced by MPES devices is challenging due to cross interference effects and complicated by the presence of interferent methane sources including wetlands and agricultural resources.
We have developed a machine learning system with artificial neural networks using MPES devices as inputs and either concentration or mixture label as outputs. We have previously demonstrated using this approach to automatically decode sensor signals associated with automotive emissions gas mixtures [3-4]. Artificial neural networks created using the Google Tensorflow library for the Python programming language were trained on MPES sensor signals to identify and quantify mixtures as natural gas, CH4 only, and a CH4+NH3 mix as a simulant for bovine emissions. The results we have collected showed >99% test accuracy in identification of these gas mixtures as shown in the confusion matrix in Figure 1 in the concentration range of 1000-4000 PPM CH4 and 75-255 PPM NH3. Optimization of the ANN architecture to minimize processing time and facilitate deployment of machine learning onto portable hardware will also be presented. We will also compare the performance of ANNs to alternate ML techniques.
This work was supported by the US Department of Energy under award DE-FOA-0031864.
[1] A.J. Marchese, T. L. Vaugh, D. J. Zimmerle, D. M. Martinez, L. L. Williams, A. L. Robinson, A. L. Mitchell, R. Subramanian, D. S. Tkacik, J. R. Roscioli, S. C. Herndon. Science. 7204 (2018).
[2] Kort, E. A.; Frankenberg, C. Costigan, K.R.; Lindenmaier, R.; Dubey, M. K; Wunch, D. Geophys. Res. Lett, 2014, 41, 19, 6898
[3] L. Tsui, A. Benavidez, P. Palanisamy, L. Evans, F. Garzon, Electrochim. Acta 283 (2018) 141–148.
[4] L. Tsui, A. Benavidez, P. Palanisamy, L. Evans, F. Garzon, Sens. Act. B: Chem. 249 (2017), 673-684
Figure 1. Confusion matrix for (a) natural gas simulant, (b) CH4, (c) NH3, and (d) CH4+NH3. Mixtures (b) and (d) serve as surrogates for wetlands and agricultural bovine emissions respectively.

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Novel Data Science Driven Chemical Agent Sensors: Towards Better Discrimination in Complex Environments
Authors: Uzarski Joshua R , Wiederoder Michael S. , Weiss Matthew , Moore Alexander , Paffenroth Randy 
Published: July 2021

Chemical agents remain a persistent threat to both Warfighters and civilian personnel in both combat and non-combat environments. While many useful detection technologies exist to provide early warning, many of them suffer from similar drawbacks. Most common are unacceptable false positive response rates and inability to discriminate target analytes in complex or obscurant-laden environments. A typical approach to this problem is to develop material solutions for more specific and sensitive sensors. While useful, these solutions are often too expensive, not practical, or non-deployable. Our approach fuses both material and data science to extend the power of multiplexed sensor arrays towards more accurate and faster detection and discrimination.
This presentation will discuss our approach towards vapor analyte detection using multiplex array systems combined with novel data science approaches. For vapor phase detection, sorptive and conductive graphene-based nanocomposite polymer films as well as select 2D conductive materials are used for semi-selective affinity towards chemical agent simulants in binary mixtures with common obscurants. Response data are pre-processed using a novel algorithm called the Variational Autoencoder Kalman Filter and the entire response dataset is used to train neural networks for analyte discrimination. To inform the neural networks to facilitate accurate and fast analyte discrimination, datasets far exceeding the capability of laboratory measurements are needed. Here too we describe our approach to generate simulated data to supplement and augment the classification algorithm machine learning process. We lastly demonstrate this approach to discriminate binary and tertiary mixtures of analytes with both similar and different chemical similarity both in and out of the precise of common environmental interferents.