Search NASA⌕ Search

Engineering topics

Connolley, Thomas

Publications and source records attributed to Connolley, Thomas.

Thermal Runaway of Li-Ion Cells: How Internal Dynamics, Mass Ejection, and Heat Vary with Cell Geometry and Abuse Type

Thermal runaway of lithium-ion batteries can involve various types of failure mechanisms each with their own unique characteristics. Using fractional thermal runaway calorimetry and high-speed radiography, the response of three different geometries of cylindrical cell (18650, 21700, and D-cell) to different abuse mechanisms (thermal, internal short circuiting, and nail penetration) are quantified and statistically examined. Correlations between the geometry of cells and their thermal behavior are identified, such as increasing heat output per amp-hour (kJ Ah −1 ) of cells with increasing cell diameter during nail penetration. High-speed radiography reveals that the rate of thermal runaway propagation within cells is generally highest for nail penetration where there is a relative increase in rate of propagation with increasing diameter, compared to thermal or internal short-circuiting abuse. For a given cell model tested under the same conditions, a distribution of heat output is observed with a trend of increasing heat output with increased mass ejection. Finally, internal temperature measurements using thermocouples embedded in the penetrating nail are shown to be unreliable thus demonstrating the need for care when using thermocouples where the temperature is rapidly changing. All data used in this manuscript are open access through the NREL and NASA Battery Failure Databank.

25 ENERGY STORAGE↗

The Effect of Cell Geometry and Trigger Method on the Risks Associated with Thermal Runaway of Lithium-Ion Batteries

Consideration of thermal runaway heat output variability is paramount for the development of safe lithium-ion battery assemblies. This study utilizes data gathered from fractional thermal runaway calorimetry (FTRC) experiments to conduct a comparative analysis of thermal runaway heat output for three cell formats (18650, 21700, and 33600) as a function of trigger method (heaters, internal short-circuiting device, and nail penetration). The analysis is based on comparisons for the calculated total energy yield, fractional energy yield, heat rate, and heat flux. This study reveals that nail penetration tends to result in higher thermal runaway heat output for larger cells (21700 & 33600); these experiments also tended to result in higher fractions of the total energy being released through the cell body. The smaller cells (18650) did not appear to have significant variation in heat output as a function of trigger method. This finding suggests that, for this cell type, worst-case scenario heat output could be achievable in assembly level testing regardless of the utilized trigger method. This study also demonstrates successful translation of FTRC results, as recorded in the Battery Failure Databank, into meaningful analysis that breaks down the influence of specific conditions on thermal runaway heat output.

calorimetry↗

Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography

Parallel-beam tomography systems at synchrotron facilities have limited field of view (FOV) determined by the available beam size and detector system coverage. Scanning the full size of samples bigger than the FOV requires various data acquisition schemes such as grid scan, 360-degree scan with offset center-of-rotation (COR), helical scan, or combinations of these schemes. Though straightforward to implement, these scanning techniques have not often been used due to the lack of software and methods to process such types of data in an easy and automated fashion. The ease of use and automation is critical at synchrotron facilities where using visual inspection in data processing steps such as image stitching, COR determination, or helical data conversion is impractical due to the large size of datasets. Here, we provide methods and their implementations in a Python package, named Algotom, for not only processing such data types but also with the highest quality possible. The efficiency and ease of use of these tools can help to extend applications of parallel-beam tomography systems.

36 MATERIALS SCIENCE↗