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• W4309595898 endingPage "120308" @default.
• W4309595898 startingPage "120308" @default.
• W4309595898 abstract "Accurate and reliable battery capacity estimation has been a key challenge to technology advancement of safety–critical systems such as electric vehicles and stationary energy storage systems. In this work, we propose a new battery capacity estimation approach using relaxation voltage data collected for only 10 s. A strong correlation is first identified between the relaxation voltage and battery capacity over the entire lifetime. Based on this key enabling correlation, a convolutional neural network model is then developed to estimate capacity for batteries with different degradation paths. The generalizability of the model is assessed by 28 batteries and the average percentage test error on 8 validation cells is only 1.8%. The high predictive power of the relaxation voltage is rationalized by demonstrating the impacts of degradation mechanisms on the ionic and electronic transport properties as the battery ages, ultimately manifesting in the relaxation voltage curves. We also show that the proposed method has the potential to be extended to batteries with different chemistries." @default.
• W4309595898 created "2022-11-28" @default.
• W4309595898 creator A5043203530 @default.
• W4309595898 creator A5078975833 @default.
• W4309595898 date "2023-01-01" @default.
• W4309595898 modified "2023-10-14" @default.
• W4309595898 title "Battery capacity estimation using 10-second relaxation voltage and a convolutional neural network" @default.
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• W4309595898 doi "https://doi.org/10.1016/j.apenergy.2022.120308" @default.
• W4309595898 hasPublicationYear "2023" @default.
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