We present measurements of controlled Li-ion battery explosions using high-speed infrared thermography to elucidate the effects of this phenomenon. In one study, commercial Li-ion batteries were perturbed by slow heating and by rapid puncture at various states of charge (SOC). The results indicate that the SOC has a significant impact on the magnitude of the battery explosion, regardless of the way the battery is perturbed. Another study tested varied heating rates in thermal abuse tests and showed that faster heating also leads to more violent thermal runaway. Within those measurements, the plumes emanating from the safety vents on the batteries were clearly observed. This work focuses on the propagation of the explosion immediately after the battery detonation event and the implications of the results for designing safer, more reliable Li-ion battery systems.
Advances in critical subsystem technologies have allowed Telops to develop the next-generation of hyperspectral imaging systems with significant reductions in Size, Weight, and Power (SWaP) requirements while maintaining imaging and data quality performance. This presentation will serve as an overview of the system architecture and analysis capabilities of the next-generation Hyper-Cam Nano hyperspectral imaging system. The Hyper-Cam Nano platform includes a miniaturized (172 x 172 x 181 mm) Fourier Transform Spectrometer (FTS) mounted on a gimbal, which can be deployed in a ground configuration, or easily affixed to an octocopter drone. The real-time data analysis capabilities embedded in the Hyper-Cam Nano provide an ability to simultaneously resolve multiple spectral signatures within a scene for the detection and identification of gases and solids, and even quantification for gases. This novel instrument will offer new capabilities in gas detection and identification applications for the defense, industrial, and environmental sectors.
Assessment of panchromatic sharpening algorithms typically starts with a high resolution Hyperspectral Image (HSI), which is then spatially degraded so that after the sharpening process, the result can be compared to the original and analyzed for accuracy. This leads to questions about quantitative assessments based solely on simulated low resolution data. To address this problem, a multi-resolution hyperspectral data set was collected by researchers from the Rochester Institute of Technology (RIT) in Henrietta, NY on July 24th 2020. Imagery of 48 felt targets, ranging in size from 5 cm to 30 cm and in six different colors, was collected using RIT's MX-1 UAS imaging system, which is designed to collect 272 spectral bands from approximately 400nm to 1000nm. Three flights were performed and images of the target scene were collected at flight altitudes of 30m, 60m, and 120m. The resulting imagery possesses ratios of 2:1 and 4:1 spatial resolution relative to the lowest altitude flight. The goal of this imaging campaign was to create a data set that will be used to test hyperspectral pansharpening algorithms currently under development at RIT. The radiometric accuracy of sharpening algorithms can be better ascertained through quantitative analysis of their results after being applied to this non-simulated multiresolution hyperspectral data set. This presentation will summarize the process of planning, creating, collecting and packaging the dataset which will be provided to RIT researchers and will also available for download online through RIT.
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