Palwinder Kaur, Isaac K. Stier, Sudeshna Bagchi, V. Pol, A. Bhondekar
{"title":"对锂离子电池在高温下释放的挥发性有机化合物进行阻抗法早期感应","authors":"Palwinder Kaur, Isaac K. Stier, Sudeshna Bagchi, V. Pol, A. Bhondekar","doi":"10.3390/batteries9120562","DOIUrl":null,"url":null,"abstract":"Lithium-ion batteries prove to be a promising technology for achieving present and future goals regarding energy resources. However, a few cases of lithium-ion battery fires and failures caused by thermal runaway have been reported in various news articles; therefore, it is important to enhance the safety of the batteries and their end users. The early detection of thermal runaway by detecting gases/volatile organic compounds (VOCs) released at the initial stages of thermal runaway can be used as a warning to end users. An interdigitated platinum electrode spin-coated with a sub-micron thick layer of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) showed sensitivity for two VOCs (ethyl-methyl carbonate and methyl formate) released from Li-ion batteries during thermal runaway, as well as their binary mixtures at elevated temperatures, which were measured using impedance spectroscopy over a frequency range of 1 MHz to 1 Hz. The sensor response was tested at three different high temperatures (40 °C, 55 °C, and 70 °C) for single analytes and binary mixtures of two VOCs at 5 ppm, 15 ppm, and 30 ppm concentrations. Equivalent electrical parameters were derived from impedance data. A machine learning approach was used to classify the sensor’s response. Classification algorithms classify the sensor’s response at elevated temperatures for different analytes with an accuracy greater than 70%. The success of the reported sensors will enhance battery safety via the early detection of thermal runaway.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":"6 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Impedimetric Early Sensing of Volatile Organic Compounds Released from Li-Ion Batteries at Elevated Temperatures\",\"authors\":\"Palwinder Kaur, Isaac K. Stier, Sudeshna Bagchi, V. Pol, A. Bhondekar\",\"doi\":\"10.3390/batteries9120562\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lithium-ion batteries prove to be a promising technology for achieving present and future goals regarding energy resources. However, a few cases of lithium-ion battery fires and failures caused by thermal runaway have been reported in various news articles; therefore, it is important to enhance the safety of the batteries and their end users. The early detection of thermal runaway by detecting gases/volatile organic compounds (VOCs) released at the initial stages of thermal runaway can be used as a warning to end users. An interdigitated platinum electrode spin-coated with a sub-micron thick layer of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) showed sensitivity for two VOCs (ethyl-methyl carbonate and methyl formate) released from Li-ion batteries during thermal runaway, as well as their binary mixtures at elevated temperatures, which were measured using impedance spectroscopy over a frequency range of 1 MHz to 1 Hz. The sensor response was tested at three different high temperatures (40 °C, 55 °C, and 70 °C) for single analytes and binary mixtures of two VOCs at 5 ppm, 15 ppm, and 30 ppm concentrations. Equivalent electrical parameters were derived from impedance data. A machine learning approach was used to classify the sensor’s response. Classification algorithms classify the sensor’s response at elevated temperatures for different analytes with an accuracy greater than 70%. The success of the reported sensors will enhance battery safety via the early detection of thermal runaway.\",\"PeriodicalId\":8755,\"journal\":{\"name\":\"Batteries\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Batteries\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.3390/batteries9120562\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ELECTROCHEMISTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Batteries","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.3390/batteries9120562","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ELECTROCHEMISTRY","Score":null,"Total":0}
Impedimetric Early Sensing of Volatile Organic Compounds Released from Li-Ion Batteries at Elevated Temperatures
Lithium-ion batteries prove to be a promising technology for achieving present and future goals regarding energy resources. However, a few cases of lithium-ion battery fires and failures caused by thermal runaway have been reported in various news articles; therefore, it is important to enhance the safety of the batteries and their end users. The early detection of thermal runaway by detecting gases/volatile organic compounds (VOCs) released at the initial stages of thermal runaway can be used as a warning to end users. An interdigitated platinum electrode spin-coated with a sub-micron thick layer of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) showed sensitivity for two VOCs (ethyl-methyl carbonate and methyl formate) released from Li-ion batteries during thermal runaway, as well as their binary mixtures at elevated temperatures, which were measured using impedance spectroscopy over a frequency range of 1 MHz to 1 Hz. The sensor response was tested at three different high temperatures (40 °C, 55 °C, and 70 °C) for single analytes and binary mixtures of two VOCs at 5 ppm, 15 ppm, and 30 ppm concentrations. Equivalent electrical parameters were derived from impedance data. A machine learning approach was used to classify the sensor’s response. Classification algorithms classify the sensor’s response at elevated temperatures for different analytes with an accuracy greater than 70%. The success of the reported sensors will enhance battery safety via the early detection of thermal runaway.