Publications

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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.

Thermal runaway of Li-ion batteries is a risk that is magnified when stacks of lithium-ion cells are used for large scale energy storage. When limits of propagation can be identified so that systems can be designed to prevent large scale cascading failure even if a failure does occur, these systems will be safer. The prediction of cell-to-cell failure propagation and the propagation limits in lithium-ion cell stacks were studied to better understand and identify safe designs. A thermal-runaway model was considered based on recent developments in thermochemical source terms. Propagating failure was characterized by temperatures above which calorimetry data is available. Results showed high temperature propagating failure predictions are too rapid unless an intra-particle diffusion limit is included, introducing a Damköhler number limiter into the rate expression. This new model form was evaluated against cell-to-cell failure propagation where the end cell of a stack is forced into thermal runaway through a nail-induced short circuit. Limits of propagation for this configuration are identified. Results showed cell-to-cell propagation predictions are consistent with measurements over a range of cell states of charge and with the introduction of metal plates between cells to add system heat capacity representative of structural members. This consistency extends from scenarios where propagation occurs through scenarios where propagation is prevented.

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Thermal runaway of lithium-ion batteries is a risk that is magnified when stacks of lithium-ion cells are used for large scale energy storage. When limits of propagation can be identified so that systems can be designed to prevent large scale cascading failure even if a failure does occur, these systems will be safer. This work addresses the prediction of cell-to-cell failure propagation and the propagation limits in lithium-ion cell stacks to better understand and identify safe designs. A thermal-runaway model is presented based on recent developments in thermochemical source terms. It is noted that propagating failure is characterized by temperatures above which calorimetry data is available. Results show high temperature propagating failure predictions are too rapid unless an intra-particle diffusion limit is included, introducing a Damköhler number limiter into the rate expression. This new model form is evaluated against cell-to-cell failure propagation where the end cell of a stack is forced into thermal runaway through a nail-induced short circuit. Limits of propagation for this configuration are identified. Results show cell-to-cell propagation predictions are consistent with measurements over a range of cell states of charge and with the introduction of metal plates between cells to add system heat capacity representative of structural members. This consistency extends from scenarios where propagation occurs through scenarios where propagation is prevented.

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Failure propagation testing is of increasing interest to the designers and end users of battery systems. One of the chief difficulties, however, is choosing an appropriate initiation method to perform the test. Single cell abuse testing is typically used to initiate thermal runaway but this can involve a large amount of additional energy injected into the system. It is assumed that this will have some impact on the behavior of a propagating thermal runaway event, but there is little data available as to how significant this would be. Further, it is ultimately difficult to develop viable propagation tests for compliance and public safety activities without better knowledge of how test methods will impact the results. This work looks at propagating battery failure with a variety of chemistries, formats, configurations and initiation methods to determine the level of significance of the chosen initiation method on the test results. We have ultimately found while there is some impact on the detailed results of propagation testing, in most cases other factors, particularly the energy density of the system play a much greater role in the likelihood of a propagation event consuming an entire battery. We have also provided some guidelines for test design to support best practices in testing.

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Li deposition at the graphitic anode is widely reported in literature as one of the leading causes of capacity fade in lithium-ion batteries (LIBs). Previous literature has linked Li deposition resulting from low-temperature ageing to diminished safety characteristics, however no current research has probed the effects of Li deposition on the abuse response of well-characterized cells. Using overtemperature testing, a relationship between increased concentrations of Li deposition and exacerbated abuse response in 1 Ah pouch cells has been established. A novel Li deposition technique is also investigated, where cells with n:p < 1 (anode-limiting) have been cycled at a high rate to exploit Li+ diffusion limitations at the anode. Scanning Electron Microscopy of harvested anodes indicates substantial Li deposition in low n:p cells after 20 cycles, with intricate networks of Li(s) deposits which hinder Li+ intercalation/deintercalation. Peak broadening and decreased amplitude of differential capacity plots further validates a loss of lithium inventory to Li+ dissolution, and Powder X-ray Diffraction indicates Li+ intercalation with staging in anode interstitial sites as the extent of Li deposition increases. A cradle-to-grave approach is leveraged on cell fabrication and testing to eliminate uncertainty involving the effects of cell additives on Li deposition and other degradation mechanisms.

The heat generated during a single cell failure within a high energy battery system can force adjacent cells into thermal runaway, creating a cascading propagation effect through the entire system. This work examines the response of modules of stacked pouch cells after thermal runaway is induced in a single cell. The prevention of cascading propagation is explored on cells with reduced states of charge and stacks with metal plates between cells. Reduced states of charge and metal plates both reduce the energy stored relative to the heat capacity, and the results show how cascading propagation may be slowed and mitigated as this varies. These propagation limits are correlated with the stored energy density. Results show significant delays between thermal runaway in adjacent cells, which are analyzed to determine intercell contact resistances and to assess how much heat energy is transmitted to cells before they undergo thermal runaway. A propagating failure of even a small pack may stretch over several minutes including delays as each cell is heated to the point of thermal runaway. This delay is described with two new parameters in the form of gap-crossing and cell-crossing time to grade the propensity of propagation from cell to cell.

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One of the first milestones of the Behind the Meter Storage (BTMS) program was to develop testing protocols so that the state-of-the-art cell chemistries and form factors could be evaluated against BTMS aggressive performance and lifetime metrics. To help guide this conversation, a pack estimation calculation was run. At the time the team was assuming a worst-case scenario in which the battery alone would need to charge an electric vehicle in 15 minutes with no support from the grid. This calculation varied the amount of current applied by each string or module in the storage system and estimated how many cells (and estimated cost) would be needed to charge an electric vehicle in 15 minutes under the current applied.

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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