Publications

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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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Lithium ion batteries have a well-documented tendency to fail energetically under various abuse conditions. These conditions frequently result in decomposition of the electrochemical components within the battery resulting in gas generation and increased internal pressure which can lead to an explosive case rupture. The 18650 format cell incorporates a vent mechanism located within a crimped cap to relieve pressure and mitigate the risk of case rupture. Cell venting, however, introduces additional safety concerns associated with the flow of flammable gases and liquid electrolyte into the environment. Experiments to quantify key parameters are performed to elucidate the external dynamics of battery venting. A first experiment measures the vent burst pressure. Burst vent caps are then tested with a second experimental fixture to measure vent opening area and discharge coefficient during choked-flow venting, which occurs during battery failure. Vent opening area and discharge coefficient are calculated from stagnation temperature, stagnation pressure, and static pressure measurements along with compressible-isentropic flow equations and conservation of mass. Commercially sourced vent caps are used with repeated tests run to quantify repeatability and variability. Validation experiments confirmed accuracy of opening area and discharge coefficient measurement. Further, trials conducted on vent caps from two sources demonstrate the potential for variation between manufacturers.

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Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This paper presents three advances in BESS SoC forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational data is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. The proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.

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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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