Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

• Published in: Nature communications Vol. 13; no. 1; pp. 2261 - 10
• Main Authors: Zhu, Jiangong, Wang, Yixiu, Huang, Yuan, Bhushan Gopaluni, R., Cao, Yankai, Heere, Michael, Mühlbauer, Martin J., Mereacre, Liuda, Dai, Haifeng, Liu, Xinhua, Senyshyn, Anatoliy, Wei, Xuezhe, Knapp, Michael, Ehrenberg, Helmut
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.04.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/301/299; 639/4077/4079/891; 639/705/1046; Cycles; Datasets; Electric cells; Electric potential; Electrodes; Humanities and Social Sciences; Linear transformations; Lithium; Lithium-ion batteries; Machine learning; multidisciplinary; Rechargeable batteries; Root-mean-square errors; Science; Science (multidisciplinary); Transfer learning; Voltage

Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprisin...