Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 조인휘 | - |
dc.date.accessioned | 2022-04-20T00:35:04Z | - |
dc.date.available | 2022-04-20T00:35:04Z | - |
dc.date.issued | 2020-08 | - |
dc.identifier.citation | CSOC 2020. Advances in Intelligent Systems and Computing, vol 1224, Intelligent Algorithms in Software Engineering, page. 178-188 | en_US |
dc.identifier.isbn | 978-3-030-51964-3 | - |
dc.identifier.isbn | 978-3-030-51965-0 | - |
dc.identifier.uri | https://link.springer.com/chapter/10.1007/978-3-030-51965-0_15 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/170130 | - |
dc.description.abstract | A lithium-ion battery is rechargeable and is widely used in portable devices and electric vehicles (EVs). State-of-Charge (SOC) estimation is vital function in a battery management system (BMS) since high-accuracy SOC estimation ensures reliability and safety of electronic products using lithium-ion batteries. Unlike traditional SOC estimation methods deep learning based methods are data-driven methods that do not rely much on battery quality. In this paper, an Encoder-Decoder model which can compress sequential inputs into a vector used for decoding sequential outputs is proposed to estimate the SOC based on measured voltage and current. Compared with conventional recurrent networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), the proposed model yields better accuracy of estimation. Models are validated on lithium-ion battery data set with dynamical stress testing (DST), Federal Urban Driving Schedule (FUDS), and US06 highway schedule profiles. | en_US |
dc.description.sponsorship | This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1I1A1A01058964). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Science + Business Media | en_US |
dc.subject | Lithium-ion battery | en_US |
dc.subject | State of charge | en_US |
dc.subject | Long Short-Term Memory | en_US |
dc.subject | Gated Recurrent Unit | en_US |
dc.subject | Encoder-Decoder | en_US |
dc.title | An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries | en_US |
dc.type | Article | en_US |
dc.relation.volume | 1224 | - |
dc.identifier.doi | 10.1007/978-3-030-51965-0_15 | - |
dc.relation.page | 178-188 | - |
dc.relation.journal | Advances in Intelligent Systems and Computing | - |
dc.contributor.googleauthor | Cui, Shengmin | - |
dc.contributor.googleauthor | Yong, Xiaowa | - |
dc.contributor.googleauthor | Kim, Sanghwan | - |
dc.contributor.googleauthor | Hong, Seokjoon | - |
dc.contributor.googleauthor | Joe, Inwhee | - |
dc.relation.code | 2020000634 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | SCHOOL OF COMPUTER SCIENCE | - |
dc.identifier.pid | iwjoe | - |
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