Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries

IF 0.5 Q4 OPTICS
Kacper Cierpiak, Marta Szczerska, Pawel Wierzba
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引用次数: 0

Abstract

Optical fiber sensors using low-coherence interferometry require processing of the output spectrum or interferogram to determine the instantaneous value of the measured quantity, such as temperature, quickly and accurately. Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber sensor of temperature is demonstrated. Using a ZnO-coated sensing interferometer and spectral detection, the sensor is intended for monitoring lithium-ion rechargeable batteries. While the performance of all methods was good, some of them seem to be better suited for this application. Full Text: PDF References B. Wrålsen et al., "Circular business models for lithium-ion batteries - Stakeholders, barriers, and drivers", J. Clean. Prod. 317, 128393 (2021), CrossRef T. Deng et al., "Measuring smartphone usage and task switching with log tracking and self-reports", Mobile Media & Communication 7, 3 (2019), CrossRef Smartphone Subscriptions Worldwide 2027. Statista, DirectLink T. Jerzyński, G. V. Stimson, H. Shapiro, G. Król., "Estimation of the global number of e-cigarette users in 2020", Harm Reduct. J. 18, 109 (2021). CrossRef Battery chemistries, DirectLink Y. Chen, et al., "A review of lithium-ion battery safety concerns: The issues, strategies, and testing standards", J. Energy Chem 59, 83 (2021), CrossRef T. Görgülü, M. Torun, A. Olgun, "A cause of severe thigh injury: Battery explosion", Ann. Med. Surg. 5, 49 (2016) CrossRef T. Maraqa et al., "Too Hot for Your Pocket! Burns From E-Cigarette Lithium Battery Explosions: A Case Series", J. Burn Care Res. 39, 1043 (2018). CrossRef Y. Chen et al., "A review of lithium-ion battery safety concerns: The issues, strategies, and testing standards", J. Energy Chem. 59, 83 (2021), CrossRef Q. Wang et al., "Thermal runaway caused fire and explosion of lithium ion battery", J. Power Sources 208, 210 (2012), CrossRef P. V. Chombo, L. Yossapong, "A review of safety strategies of a Li-ion battery", J. Power Sources 478, 228649 (2020), CrossRef P. Listewnik, M. Bechelany, J. B. Jasinski, M. Szczerska, "ZnO ALD-Coated Microsphere-Based Sensors for Temperature Measurements", Sensors 20, 4689 (2020), CrossRef M. Szczerska, "Temperature Sensors Based on Polymer Fiber Optic Interferometer", Chemosensors 10, 228 (2022), CrossRef M. Kruczkowski et al., "redictions of cervical cancer identification by photonic method combined with machine learning", Sci. Rep. 12, 3762 (2022), CrossRef
使用机器学习算法的光纤传感器在锂离子电池温度测量中的应用
采用低相干干涉测量的光纤传感器需要对输出光谱或干涉图进行处理,以快速准确地确定被测量(如温度)的瞬时值。基于机器学习的方法是这种应用的一个很好的候选。介绍了这四种方法在光纤温度传感器中的应用。该传感器采用zno涂层传感干涉仪和光谱检测,用于监测锂离子可充电电池。虽然所有方法的性能都很好,但其中一些方法似乎更适合这个应用程序。B. wratlsen等,“锂离子电池的循环商业模式——利益相关者、障碍和驱动因素”,J. Clean。317,128393 (2021), CrossRef . Deng et al.,“用日志跟踪和自我报告测量智能手机使用和任务切换”,移动媒体&;通信7,3 (2019),CrossRef全球智能手机订阅2027。Statista, DirectLink T. Jerzyński, g.v. Stimson, H. Shapiro, G. Król。《2020年全球电子烟用户数量估计》,《减少危害》。J. 18, 109(2021)。陈毅,等,“锂离子电池的安全问题:问题、策略和测试标准”,能源化学学报,59 (2021),CrossRef . Görgülü, M. Torun, A. Olgun,“严重大腿损伤的原因:电池爆炸”,安。医学外科学,2016,49 (2016)CrossRef T. Maraqa et al.,“太热了!电子烟锂电池爆炸致烧伤的研究进展[j] .中国烧伤防治杂志,2018,34(6)。CrossRef . Chen等人,“锂离子电池安全问题综述:CrossRef P. V. Chombo, L. Yossapong,“一种锂离子电池的安全策略综述”,J. Bechelany, J. B. Jasinski, M. Szczerska,“基于ZnO ald涂层的微球温度测量传感器”,J.能源化学,59,83 (2021),CrossRef Q. Wang等,“热失控引起的锂离子电池火灾和爆炸”,J. Power Sources, 208, 210 (2012), CrossRef P. V. Chombo, L. Yossapong,“J. Power Sources, 478, 228649(2020)”,CrossRef P. Listewnik, M. Bechelany, J. B. Jasinski, M. Szczerska,“基于ZnO ald涂层的微球温度测量传感器”,传感器20,4689(2020)。CrossRef M. Szczerska,“基于聚合物光纤干涉仪的温度传感器”,化学传感器10,228 (2022),CrossRef M. Kruczkowski等,“结合机器学习的光子方法对宫颈癌识别的预测”,Sci。众议员12,3762(2022),交叉参考
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
24
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