{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T19:08:58Z","timestamp":1723403338263},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,22]]},"abstract":"<jats:p>State-of-Health (SOH) prediction of a Lithium-ion battery is essential for preventing malfunction and maintaining efficient working behaviors for the battery. In practice, this task is difficult due to the high level of noise and complexity. There are many machine learning methods, especially deep learning approaches, that have been proposed to address this problem recently. However, there is much room for improvement because the nature of the battery data is highly non-linear and exhibits higher dependence on multidisciplinary parameters such as resistance, voltage and external conditions the battery is subjected to. In this paper, we propose an approach known as bidirectional sequence-in-sequence, which exploits the dependency of nested cycle-wise and channel-wise battery data. Experimented with real dataset acquired from NASA, our method results in significant reduction of error of approximately up to 32.5%.<\/jats:p>","DOI":"10.3233\/faia210385","type":"book-chapter","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T10:20:08Z","timestamp":1640773208000},"source":"Crossref","is-referenced-by-count":2,"title":["Sequence-in-Sequence Learning for SOH Estimation of Lithium-Ion Battery"],"prefix":"10.3233","author":[{"given":"Thien","family":"Pham","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City, Vietnam"}]},{"given":"Loi","family":"Truong","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City, Vietnam"}]},{"given":"Mao","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Ho Chi Minh City University of Computer Science, Vietnam National University Ho Chi Minh City, Vietnam"}]},{"given":"Akhil","family":"Garg","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology Wuhan, China"}]},{"given":"Liang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology Wuhan, China"}]},{"given":"Tho","family":"Quan","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City, Vietnam"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Proceedings of CECNet 2021"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210385","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T10:20:10Z","timestamp":1640773210000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,22]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210385","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,22]]}}}