Research on an optical fiber sensor for real-time monitoring of gas pressure inside lithium batteries

Xuefeng Shi; Xinglin Tong; Cui Zhang; Wenzhong Huang; Fanchao Zeng; Chengfei Li; Yuhan Wang; Long Chen

doi:10.1117/12.3032881

5 July 2024 Research on an optical fiber sensor for real-time monitoring of gas pressure inside lithium batteries

Xuefeng Shi, Xinglin Tong, Cui Zhang, Wenzhong Huang, Fanchao Zeng, Chengfei Li, Yuhan Wang, Long Chen

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131840O (2024) https://doi.org/10.1117/12.3032881
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Thermal runaway of lithium batteries is a matter of great concern to relevant scholars. The gas pressure during the charge and discharge control process of lithium batteries is directly related to the performance of lithium batteries. Relevant scholars have been pursuing a method that can safely monitor the internal gas pressure of lithium batteries. Based on the characteristics of optical fiber sensors such as small size, anti-electromagnetic interference, and intrinsic safety, this paper designs and prepares an optical fiber gas sensor that can be used to measure the internal gas of lithium batteries. The measurement range of the sensor is 0-240kPa, and the resolution of the sensor is 0.5kPa, the sensor's accuracy reaches 5kPa, which can be used as a feasible detection method for gas generated during the working process of lithium batteries.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuefeng Shi, Xinglin Tong, Cui Zhang, Wenzhong Huang, Fanchao Zeng, Chengfei Li, Yuhan Wang, and Long Chen "Research on an optical fiber sensor for real-time monitoring of gas pressure inside lithium batteries", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131840O (5 July 2024); https://doi.org/10.1117/12.3032881

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