Publications

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Mixed-acid vanadium redox flow batteries (VRFBs) are an attractive option to increase energy density and temperature stability relative to conventional VRFBs for grid energy storage applications. However, the inclusion of hydrochloric acid introduces a significant safety risk through chlorine gas (Cl2) evolution. Here, we present the first direct measurements of Cl2 generation in a mixed-acid VRFB. Cl2 is generated through an electrochemical reaction when the system is charged above ∼74% state of charge with concentrations exceeding 3% of the system headspace. We explore how Cl2 evolution is enabled and propose mitigation strategies.

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Lithium-ion batteries are typically modeled using porous electrode theory coupled with various transport and reaction mechanisms, along with suitable discretization or approximations for the solid-phase diffusion equation. The solid-phase diffusion equation represents the main computational burden for typical pseudo-2-dimensional (p2D) models since these equations in the pseudo r-dimension must be solved at each point in the computational grid. This substantially increases the complexity of the model as well as the computational time. Traditional approaches towards simplifying solid-phase diffusion possess certain significant limitations, especially in modeling emerging electrode materials which involve phase changes and variable diffusivities. A computationally efficient representation for solid-phase diffusion is discussed in this paper based on symmetric polynomials using Orthogonal Collocation and Galerkin formulation (weak form). A systematic approach is provided to increase the accuracy of the approximation (p form in finite element methods) to enable efficient simulation with a minimal number of semi-discretized equations, ensuring mass conservation even for non-linear diffusion problems involving variable diffusivities. These methods are then demonstrated by incorporation into the full p2D model, illustrating their advantages in simulating high C-rates and short-time dynamic operation of Lithium-ion batteries.

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Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on “knees” in lithium-ion battery aging trajectories. We first review definitions for knees and three classes of “internal state trajectories” (termed snowball, hidden, and threshold trajectories) that can cause a knee. We then discuss six knee “pathways”, including lithium plating, electrode saturation, resistance growth, electrolyte and additive depletion, percolation-limited connectivity, and mechanical deformation—some of which have internal state trajectories with signals that are electrochemically undetectable. Additionally, we also identify key design and usage sensitivities for knees. Finally, we discuss challenges and opportunities for knee modeling and prediction. Our findings illustrate the complexity and subtlety of lithium-ion battery degradation and can aid both academic and industrial efforts to improve battery lifetime.

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Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.

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All-solid-state batteries are often assumed to be safer than conventional Li-ion ones. In this work, we present the first thermodynamic models to quantitatively evaluate solid-state and Li-ion battery heat release under several failure scenarios. The solid-state battery analysis is carried out with an Li7La3Zr2O12 solid electrolyte but can be extended to other configurations using the accompanying spreadsheet. We consider solid-state batteries that include a relatively small amount of liquid electrolyte, which is often added at the cathode to reduce interfacial resistance. While the addition of small amounts of liquid electrolyte increases heat release under specific failure scenarios, it may be small enough that other considerations, such as manufacturability and performance, are more important commercially. We show that short-circuited all-solid-state batteries can reach temperatures significantly higher than conventional Li-ion, which could lead to fire through flammable packaging and/or nearby materials. Our work highlights the need for quantitative safety analyses of solid-state batteries.

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Concerns about the safety of lithium-ion batteries have motivated numerous studies on the response of fresh cells to abusive, off-nominal conditions, but studies on aged cells are relatively rare. This perspective considers all open literature on the thermal, electrical, and mechanical abuse response of aged lithium-ion cells and modules to identify critical changes in their behavior relative to fresh cells. We outline data gaps in aged cell safety, including electrical and mechanical testing, and module-level experiments. Understanding how the abuse response of aged cells differs from fresh cells will enable the design of more effective energy storage failure mitigation systems.

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Propagating thermal runaway events are a significant threat to utility-scale storage installations. A propagating thermal runaway event is a cascading series of failures in which energy released from a failed cell triggers subsequent failures in nearby cells. Without intervention, propagation can turn an otherwise manageable single cell failure into a full system conflagration. This study presents a method of mitigating the severity of propagating thermal runaway events in utility-scale storage systems by leveraging the capabilities of a module-interfaced power conversion architecture. The method involves strategic depletion of storage modules to delay or arrest propagation, reducing the total thermal energy released in the failure event. The feasibility of the method is assessed through simulations of propagating thermal runaway events in a 160 kW/80 kWh energy storage system.

Propagating thermal runaway events are a significant threat to utility-scale storage installations. A propagating thermal runaway event is a cascading series of failures in which energy released from a failed cell triggers subsequent failures in nearby cells. Without intervention, propagation can turn an otherwise manageable single cell failure into a full system conflagration. This study presents a method of mitigating the severity of propagating thermal runaway events in utility-scale storage systems by leveraging the capabilities of a module-interfaced power conversion architecture. The method involves strategic depletion of storage modules to delay or arrest propagation, reducing the total thermal energy released in the failure event. The feasibility of the method is assessed through simulations of propagating thermal runaway events in a 160 kW/80 kWh energy storage system.

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Energy storage technologies are positioned to play a substantial role in power delivery systems. They have the potential to serve as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, because of their relative infancy, there is a lack of knowledge about how these resources truly operate over time. A data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and a data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verify the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data that the provider supplies for the analysis. After a review of energy storage data received from several providers, some of these data have clearly shown to be inconsistent and incomplete, raising the question of their efficacy for a robust analysis. This report reviews and proposes general guidelines, such as sampling rates and data points, that providers must supply for a robust data analysis to take place. Consistent guidelines are the basis of a proper protocol and ensuing standards to (1) reduce the time that it takes for data to reach those who are providing the analysis; (2) allow them to better understand the energy storage installations; and (3) enable them to provide a high-quality analysis of the installations. The report is intended to serve as a starting point for what data points should be provided when monitoring. Readers are encouraged to use the guidance in the report to develop specifications for new systems, as well as enhance current efforts to ensure optimal storage performance. As battery technologies continue to advance and the industry expands, the report will be updated to remain current.

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This work uses accelerating rate calorimetry to evaluate the impact of cell chemistry, state of charge, cell capacity, and ultimately cell energy density on the total energy release and peak heating rates observed during thermal runaway of Li-ion batteries. While the traditional focus has been using calorimetry to compare different chemistries in cells of similar sizes, this work seeks to better understand how applicable small cell data is to understand the thermal runaway behavior of large cells as well as determine if thermal runaway behaviors can be more generally tied to aspects of lithium-ion cells such as total stored energy and specific energy. We have found a strong linear correlation between the total enthalpy of the thermal runaway process and the stored energy of the cell, apparently independent of cell size and state of charge. We have also shown that peak heating rates and peak temperatures reached during thermal runaway events are more closely tied to specific energy, increasing exponentially in the case of peak heating rates.

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Applications of fiber optic sensors to battery monitoring have been increasing due to the growing need of enhanced battery management systems with accurate state estimations. The goal of this review is to discuss the advancements enabling the practical implementation of battery internal parameter measurements including local temperature, strain, pressure, and refractive index for general operation, as well as the external measurements such as temperature gradients and vent gas sensing for thermal runaway imminent detection. A reasonable matching is discussed between fiber optic sensors of different range capabilities with battery systems of three levels of scales, namely electric vehicle and heavy-duty electric truck battery packs, and grid-scale battery systems. The advantages of fiber optic sensors over electrical sensors are discussed, while electrochemical stability issues of fiber-implanted batteries are critically assessed. This review also includes the estimated sensing system costs for typical fiber optic sensors and identifies the high interrogation cost as one of the limitations in their practical deployment into batteries. Finally, future perspectives are considered in the implementation of fiber optics into high-value battery applications such as grid-scale energy storage fault detection and prediction systems.

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In recent years, the pervasive use of lithium ion (Li-ion) batteries in applications such as cell phones, laptop computers, electric vehicles, and grid energy storage systems has prompted the development of specialized battery management systems (BMS). The primary goal of a BMS is to maintain a reliable and safe battery power source while maximizing the calendar life and performance of the cells. To maintain safe operation, a BMS should be programmed to minimize degradation and prevent damage to a Li-ion cell, which can lead to thermal runaway. Cell damage can occur over time if a BMS is not properly configured to avoid overcharging and discharging. To prevent cell damage, efficient and accurate cell charging cycle characteristics algorithms must be employed. In this paper, computationally efficient and accurate ensemble learning algorithms capable of detecting Li-ion cell charging irregularities are described. Additionally, it is shown using machine and deep learning that it is possible to accurately and efficiently detect when a cell has experienced thermal and electrical stress due to cell overcharging by measuring charging cycle divergence.

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Changes in the Demand Profile and a growing role for renewable and distributed generation are leading to rapid evolution in the electric grid. These changes are beginning to considerably strain the transmission and distribution infrastructure. Utilities are increasingly recognizing that the integration of energy storage in the grid infrastructure will help manage intermittency and improve grid reliability. This recognition, coupled with the proliferation of state-level renewable portfolio standards and rapidly declining lithium-ion (Li-ion) battery costs, has led to a surge in the deployment of battery energy storage systems (BESSs). Additionally, although BESSs represented less than 1% of grid-scale energy storage in the United States in 2019, they are the preferred technology to meet growing demand because they are modular, scalable, and easy to deploy across diverse use cases and geographic locations.

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Li-ion batteries currently dominate electrochemical energy storage for grid-scale applications, but there are promising aqueous battery technologies on the path to commercial adoption. Though aqueous batteries are considered lower risk, they can still undergo problematic degradation processes. This perspective details the degradation that aqueous batteries can experience during normal and abusive operation, and how these processes can even lead to cascading failure. We outline methods for studying these phenomena at the material and single-cell level. Considering reliability and safety studies early in technology development will facilitate translation of emerging aqueous batteries from the lab to the field.

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Energy storage systems with Li-ion batteries are increasingly deployed to maintain a robust and resilient grid and facilitate the integration of renewable energy resources. However, appropriate selection of cells for different applications is difficult due to limited public data comparing the most commonly used off-the-shelf Li-ion chemistries under the same operating conditions. This article details a multi-year cycling study of commercial LiFePO4 (LFP), LiNixCoyAl1-x-yO2 (NCA), and LiNixMnyCo1-x-yO2 (NMC) cells, varying the discharge rate, depth of discharge (DOD), and environment temperature. The capacity and discharge energy retention, as well as the round-trip efficiency, were compared. Even when operated within manufacturer specifications, the range of cycling conditions had a profound effect on cell degradation, with time to reach 80% capacity varying by thousands of hours and cycle counts among cells of each chemistry. The degradation of cells in this study was compared to that of similar cells in previous studies to identify universal trends and to provide a standard deviation for performance. All cycling files have been made publicly available at batteryarchive.org, a recently developed repository for visualization and comparison of battery data, to facilitate future experimental and modeling efforts.

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This paper takes a critical look at the materials aspects of thermal runaway of lithium-ion batteries and correlates contributions from individual cell components to thermal runaway trends. An accelerating rate calorimeter (ARC) was used to evaluate commercial lithium-ion cells based on LiCoO2 (LCO), LiFePO4 (LFP), and LiNixCoyAl1-x-yO2 (NCA) at various states of charge (SOC). Cells were disassembled and the component properties were evaluated by thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and temperature-resolved X-ray diffraction (TR-XRD). The whole cell thermal runaway onset temperature decreases and peak heating rate increases with SOC due to cathode destabilization. LCO and NCA cathodes are metastable, with NCA cells exhibiting the highest thermal runaway rates. By contrast, the LFP cathode is stable to >500 °C, even when charged. For anodes, the decomposition and whole cell self-heating onset temperature is generally independent of SOC. DSC exotherm onset temperatures of the anodes were generally within 10 °C of the onset of self-heating in whole cell ARC. However, onset temperatures of the cathodes were typically observed above the ARC onset of whole cell runaway. This systematic evaluation of component to whole cell degradation provides a scientific basis for future thermal modeling and design of safer cells.

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Lithium-ion battery safety is prerequisite for applications from consumer electronics to grid energy storage. Cell and component-level calorimetry studies are central to safety evaluations. Qualitative empirical comparisons have been indispensable in understanding decomposition behavior. More systematic calorimetry studies along with more comprehensive measurements and reporting can lead to more quantitative mechanistic understanding. This mechanistic understanding can facilitate improved designs and predictions for scenarios that are difficult to access experimentally, such as system-level failures. Recommendations are made to improve usability of calorimetry results in mechanistic understanding. From our perspective, this path leads to a more mature science of battery safety.

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