Yanli Liu, Yu Su, Shaofan Zhang, Vladimir Terzija, Ze Cheng
{"title":"深度学习图像识别在锂电池健康状态评估中的应用","authors":"Yanli Liu, Yu Su, Shaofan Zhang, Vladimir Terzija, Ze Cheng","doi":"10.1049/enc2.70016","DOIUrl":null,"url":null,"abstract":"<p>Accurately estimating the State of Health (SOH) of lithium-ion batteries is essential for ensuring their reliable operation. The constant-current charging voltage curves of batteries at different aging levels show significant deviations. Traditional methods based on one-dimensional time-series data face limitations in capturing and characterizing these complex patterns. To address this issue, this paper leverages the one-dimensional (1D) time series data of the lithium battery constant-current charging voltage segment, selected using incremental capacity analysis. This data is then transformed into a two-dimensional representation using the Gramian angular summation field algorithm. Utilizing the exceptional image-recognition capabilities of ResNet, this approach achieves high-accuracy SOH estimation. Validation using publicly available datasets from the University of Oxford and the University of Maryland demonstrates a significant improvement in battery SOH estimation accuracy compared to traditional techniques, which directly input voltage segments into the network.</p>","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"6 4","pages":"246-255"},"PeriodicalIF":0.0000,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70016","citationCount":"0","resultStr":"{\"title\":\"Application of deep learning image recognition for lithium battery State of Health assessment\",\"authors\":\"Yanli Liu, Yu Su, Shaofan Zhang, Vladimir Terzija, Ze Cheng\",\"doi\":\"10.1049/enc2.70016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Accurately estimating the State of Health (SOH) of lithium-ion batteries is essential for ensuring their reliable operation. The constant-current charging voltage curves of batteries at different aging levels show significant deviations. Traditional methods based on one-dimensional time-series data face limitations in capturing and characterizing these complex patterns. To address this issue, this paper leverages the one-dimensional (1D) time series data of the lithium battery constant-current charging voltage segment, selected using incremental capacity analysis. This data is then transformed into a two-dimensional representation using the Gramian angular summation field algorithm. Utilizing the exceptional image-recognition capabilities of ResNet, this approach achieves high-accuracy SOH estimation. Validation using publicly available datasets from the University of Oxford and the University of Maryland demonstrates a significant improvement in battery SOH estimation accuracy compared to traditional techniques, which directly input voltage segments into the network.</p>\",\"PeriodicalId\":100467,\"journal\":{\"name\":\"Energy Conversion and Economics\",\"volume\":\"6 4\",\"pages\":\"246-255\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70016\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy Conversion and Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/enc2.70016\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Conversion and Economics","FirstCategoryId":"1085","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/enc2.70016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of deep learning image recognition for lithium battery State of Health assessment
Accurately estimating the State of Health (SOH) of lithium-ion batteries is essential for ensuring their reliable operation. The constant-current charging voltage curves of batteries at different aging levels show significant deviations. Traditional methods based on one-dimensional time-series data face limitations in capturing and characterizing these complex patterns. To address this issue, this paper leverages the one-dimensional (1D) time series data of the lithium battery constant-current charging voltage segment, selected using incremental capacity analysis. This data is then transformed into a two-dimensional representation using the Gramian angular summation field algorithm. Utilizing the exceptional image-recognition capabilities of ResNet, this approach achieves high-accuracy SOH estimation. Validation using publicly available datasets from the University of Oxford and the University of Maryland demonstrates a significant improvement in battery SOH estimation accuracy compared to traditional techniques, which directly input voltage segments into the network.