Publications

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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.

This work demonstrates how staged heat release from layered metal oxide cathodes in the presence of organic electrolytes can be predicted from basic thermodynamic properties. These prediction methods for heat release are an advancement compared to typical modeling approaches for thermal runaway in lithium-ion batteries, which tend to rely exclusively on calorimetry measurements of battery components. These calculations generate useful new insights when compared to calorimetry measurements for lithium cobalt oxide (LCO) as well as the most common varieties of nickel manganese cobalt oxide (NMC) and nickel cobalt aluminum oxide (NCA). Accurate trends in heat release with varying state of charge are predicted for all of these cathode materials. These results suggest that thermodynamic calculations utilizing a recently published database of properties are broadly applicable for predicting decomposition behavior of layered metal oxide cathodes. Aspects of literature calorimetry measurements relevant to thermal runaway modeling are identified and classified as thermodynamic or kinetic effects. The calorimetry measurements reviewed in this work will be useful for development of a new generation of thermal runaway models targeting applications where accurate maximum cell temperatures are required to predict cascading cell-to-cell propagation rates.

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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Accurate models of thermal runaway in lithium-ion batteries require quantitative knowledge of heat release during thermochemical processes. A capability to predict at least some aspects of heat release for a wide variety of candidate materials a priori is desirable. This work establishes a framework for predicting staged heat release from basic thermodynamic properties for layered metal-oxide cathodes. Available enthalpies relevant to thermal decomposition of layered metal-oxide cathodes are reviewed and assembled in this work to predict potential heat release in the presence of alkyl-carbonate electrolytes with varying state of charge. Cathode delithiation leads to a less stable metal oxide subject to phase transformations including oxygen release when heated. We recommend reaction enthalpies and show the thermal consequences of metal-oxide phase changes and solvent oxidation within the battery are of comparable magnitudes. Heats of reaction are related in this work to typical observations reported in the literature for species characterization and calorimetry. The methods and assembled databases of formation and reaction enthalpies in this work lay groundwork a new generation of thermal runaway models based on fundamental material thermodynamics, capable of predicting accurate maximum cell temperatures and hence cascading cell-to-cell propagation rates.

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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.