Presentation + Paper
5 May 2017 Life cycle monitoring of lithium-ion polymer batteries using cost-effective thermal infrared sensors with applications for lifetime prediction
Xunfei Zhou, Anav Malik, Sheng-Jen Hsieh
Author Affiliations +
Abstract
Lithium-ion batteries have become indispensable parts of our lives for their high-energy density and long lifespan. However, failure due to from abusive usage conditions, flawed manufacturing processes, and aging and adversely affect battery performance and even endanger people and property. Therefore, battery cells that are failing or reaching their end-of-life need to be replaced. Traditionally, battery lifetime prediction is achieved by analyzing data from current, voltage and impedance sensors. However, such a prognostic system is expensive to implement and requires direct contact. In this study, low-cost thermal infrared sensors were used to acquire thermographic images throughout the entire lifetime of small scale lithium-ion polymer batteries (410 cycles). The infrared system (non-destructive) took temperature readings from multiple batteries during charging and discharging cycles of 1C. Thermal characteristics of the batteries were derived from the thermographic images. A time-dependent and spatially resolved temperature mapping was obtained and quantitatively analyzed. The developed model can predict cycle number using the first 10 minutes of surface temperature data acquired through infrared imaging at the beginning of the cycle, with an average error rate of less than 10%. This approach can be used to correlate thermal characteristics of the batteries with life cycles, and to propose cost-effective thermal infrared imaging applications in battery prognostic systems.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xunfei Zhou, Anav Malik, and Sheng-Jen Hsieh "Life cycle monitoring of lithium-ion polymer batteries using cost-effective thermal infrared sensors with applications for lifetime prediction", Proc. SPIE 10214, Thermosense: Thermal Infrared Applications XXXIX, 102140A (5 May 2017); https://doi.org/10.1117/12.2262762
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KEYWORDS
Data modeling

Thermal modeling

Sensors

Performance modeling

Temperature metrology

Statistical modeling

Infrared imaging

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