An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cui, Shengmin | - |
dc.contributor.author | Yong, Xiaowa | - |
dc.contributor.author | Kim, Sanghwan | - |
dc.contributor.author | Hong, Seokjoon | - |
dc.contributor.author | Joe, Inwhee | - |
dc.date.accessioned | 2022-07-07T22:13:40Z | - |
dc.date.available | 2022-07-07T22:13:40Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.issn | 2194-5357 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145399 | - |
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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer | - |
dc.title | An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Joe, Inwhee | - |
dc.identifier.doi | 10.1007/978-3-030-51965-0_15 | - |
dc.identifier.scopusid | 2-s2.0-85089723212 | - |
dc.identifier.bibliographicCitation | Advances in Intelligent Systems and Computing, v.1224 AISC, pp.178 - 188 | - |
dc.relation.isPartOf | Advances in Intelligent Systems and Computing | - |
dc.citation.title | Advances in Intelligent Systems and Computing | - |
dc.citation.volume | 1224 AISC | - |
dc.citation.startPage | 178 | - |
dc.citation.endPage | 188 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Charging (batteries) | - |
dc.subject.keywordPlus | Decoding | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Ions | - |
dc.subject.keywordPlus | Lithium-ion batteries | - |
dc.subject.keywordPlus | Long short-term memory | - |
dc.subject.keywordPlus | Signal encoding | - |
dc.subject.keywordPlus | Software engineering | - |
dc.subject.keywordPlus | Statistical tests | - |
dc.subject.keywordPlus | Data-driven methods | - |
dc.subject.keywordPlus | Electric Vehicles (EVs) | - |
dc.subject.keywordPlus | Electronic product | - |
dc.subject.keywordPlus | Learning-based methods | - |
dc.subject.keywordPlus | Measured voltages | - |
dc.subject.keywordPlus | Recurrent networks | - |
dc.subject.keywordPlus | Reliability and safeties | - |
dc.subject.keywordPlus | State-of-charge estimation | - |
dc.subject.keywordPlus | Battery management systems | - |
dc.subject.keywordAuthor | Encoder-Decoder | - |
dc.subject.keywordAuthor | Gated Recurrent Unit | - |
dc.subject.keywordAuthor | Lithium-ion battery | - |
dc.subject.keywordAuthor | Long Short-Term Memory | - |
dc.subject.keywordAuthor | State of charge | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-030-51965-0_15 | - |
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