D. Pelosi , F. Gallorini , P.A. Ottaviano , L. Barelli
{"title":"基于离散小波变换的锂离子电池健康状态实时评估:工作温度的影响","authors":"D. Pelosi , F. Gallorini , P.A. Ottaviano , L. Barelli","doi":"10.1016/j.powera.2024.100136","DOIUrl":null,"url":null,"abstract":"<div><p>Li-ion batteries (LIBs), thanks to high efficiencies and energy density, represent the mainstream technology to replace traditional internal combustion vehicles with electric ones. However, LIBs state of health (SoH) should be investigated to avoid fast degradation due to fast-charging, electrical, mechanical and thermal factors. Therefore, SoH prediction and monitoring for battery electric vehicles is necessary for extending LIB lifespan and avoiding failures. In this paper, an accurate real-time SoH prediction and monitoring method, based on discrete wavelet (DWT) analysis, is investigated through an extensive experimental campaign considering the effect of temperature variation. Specifically, moving from cycle aging performed on Li-ion NCR 18650 cells and applying two typical US test drive cycles at different SoHs, three different operating temperatures (i.e., 0 °C, 20 °C and 30 °C) were investigated. Applying DWT on the gathered LIB voltage profiles, it is demonstrated that temperature effect on the implemented method is easily recognizable from the one of cycle aging. Moreover, suitable linearized functions are identified to refer DWT outcomes assessed at the operative temperature to a reference temperature, at which a suitable equation is previously identified to assess capacity fading. Due to its general validity the method can be extended to stationary applications.</p></div>","PeriodicalId":34318,"journal":{"name":"Journal of Power Sources Advances","volume":"26 ","pages":"Article 100136"},"PeriodicalIF":5.4000,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666248524000027/pdfft?md5=9e6250cc994991178b823aff34d081e1&pid=1-s2.0-S2666248524000027-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Real-time Lithium-ion battery state of health evaluation based on discrete wavelet transform: The effect of operating temperature\",\"authors\":\"D. Pelosi , F. Gallorini , P.A. Ottaviano , L. Barelli\",\"doi\":\"10.1016/j.powera.2024.100136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Li-ion batteries (LIBs), thanks to high efficiencies and energy density, represent the mainstream technology to replace traditional internal combustion vehicles with electric ones. However, LIBs state of health (SoH) should be investigated to avoid fast degradation due to fast-charging, electrical, mechanical and thermal factors. Therefore, SoH prediction and monitoring for battery electric vehicles is necessary for extending LIB lifespan and avoiding failures. In this paper, an accurate real-time SoH prediction and monitoring method, based on discrete wavelet (DWT) analysis, is investigated through an extensive experimental campaign considering the effect of temperature variation. Specifically, moving from cycle aging performed on Li-ion NCR 18650 cells and applying two typical US test drive cycles at different SoHs, three different operating temperatures (i.e., 0 °C, 20 °C and 30 °C) were investigated. Applying DWT on the gathered LIB voltage profiles, it is demonstrated that temperature effect on the implemented method is easily recognizable from the one of cycle aging. Moreover, suitable linearized functions are identified to refer DWT outcomes assessed at the operative temperature to a reference temperature, at which a suitable equation is previously identified to assess capacity fading. Due to its general validity the method can be extended to stationary applications.</p></div>\",\"PeriodicalId\":34318,\"journal\":{\"name\":\"Journal of Power Sources Advances\",\"volume\":\"26 \",\"pages\":\"Article 100136\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-01-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666248524000027/pdfft?md5=9e6250cc994991178b823aff34d081e1&pid=1-s2.0-S2666248524000027-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Power Sources Advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666248524000027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Power Sources Advances","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666248524000027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Real-time Lithium-ion battery state of health evaluation based on discrete wavelet transform: The effect of operating temperature
Li-ion batteries (LIBs), thanks to high efficiencies and energy density, represent the mainstream technology to replace traditional internal combustion vehicles with electric ones. However, LIBs state of health (SoH) should be investigated to avoid fast degradation due to fast-charging, electrical, mechanical and thermal factors. Therefore, SoH prediction and monitoring for battery electric vehicles is necessary for extending LIB lifespan and avoiding failures. In this paper, an accurate real-time SoH prediction and monitoring method, based on discrete wavelet (DWT) analysis, is investigated through an extensive experimental campaign considering the effect of temperature variation. Specifically, moving from cycle aging performed on Li-ion NCR 18650 cells and applying two typical US test drive cycles at different SoHs, three different operating temperatures (i.e., 0 °C, 20 °C and 30 °C) were investigated. Applying DWT on the gathered LIB voltage profiles, it is demonstrated that temperature effect on the implemented method is easily recognizable from the one of cycle aging. Moreover, suitable linearized functions are identified to refer DWT outcomes assessed at the operative temperature to a reference temperature, at which a suitable equation is previously identified to assess capacity fading. Due to its general validity the method can be extended to stationary applications.