Design of State of Health Prediction Model for Retired High Power LiNiMnCoO2 Cell with Holts Exponential Smoothing Method
DC Field | Value | Language |
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dc.contributor.author | Lee, H. | - |
dc.contributor.author | Park, J.-H. | - |
dc.contributor.author | Kim, J. | - |
dc.contributor.author | Kim, T. | - |
dc.date.available | 2020-09-09T06:05:09Z | - |
dc.date.created | 2020-09-05 | - |
dc.date.issued | 2020-02 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/38605 | - |
dc.description.abstract | In order to recycle retired battery, it is necessary to know the state of health (SOH) of the retired battery correctly. However, as the battery ages, nonlinearity of the parameter representing SOH becomes more severe. So, the different estimation method is required from the SOH estimation method of a fresh battery. The parameters representing SOH such as discharge capacity, internal resistance, peak point of incremental capacity (IC) curve values that change with aging are time-series data. The SOH estimation of retired batteries requires a technique to analyze nonlinear time-series data. This paper presents design of SOH prediction model for retired high power LiNiMnCoO2 (NCM) cell with holt's exponential smoothing (ES) method. The holt's EX method is the method of nonelinear time-series data analysis. And, the result of the SOH prediction model with the holt's ES is compared with linear regression analysis (LRA) and the moving average (MA) method. © 2020 ACM. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.relation.isPartOf | ACM International Conference Proceeding Series | - |
dc.title | Design of State of Health Prediction Model for Retired High Power LiNiMnCoO2 Cell with Holts Exponential Smoothing Method | - |
dc.type | Article | - |
dc.identifier.doi | 10.1145/3385209.3385229 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | ACM International Conference Proceeding Series, pp.152 - 155 | - |
dc.description.journalClass | 1 | - |
dc.identifier.scopusid | 2-s2.0-85086139559 | - |
dc.citation.endPage | 155 | - |
dc.citation.startPage | 152 | - |
dc.citation.title | ACM International Conference Proceeding Series | - |
dc.contributor.affiliatedAuthor | Park, J.-H. | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Exponential smoothing | - |
dc.subject.keywordAuthor | Retired battery | - |
dc.subject.keywordAuthor | State of health (SOH) | - |
dc.subject.keywordAuthor | Time-series data analysis | - |
dc.subject.keywordPlus | Battery management systems | - |
dc.subject.keywordPlus | Electronic Waste | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Lithium compounds | - |
dc.subject.keywordPlus | Lithium-ion batteries | - |
dc.subject.keywordPlus | Manganese compounds | - |
dc.subject.keywordPlus | Regression analysis | - |
dc.subject.keywordPlus | Time series | - |
dc.subject.keywordPlus | Time series analysis | - |
dc.subject.keywordPlus | Discharge capacities | - |
dc.subject.keywordPlus | Estimation methods | - |
dc.subject.keywordPlus | Exponential smoothing | - |
dc.subject.keywordPlus | Exponential smoothing method | - |
dc.subject.keywordPlus | Internal resistance | - |
dc.subject.keywordPlus | Nonlinear time series | - |
dc.subject.keywordPlus | Time series data analysis | - |
dc.subject.keywordPlus | Time-series data | - |
dc.subject.keywordPlus | Nickel compounds | - |
dc.description.journalRegisteredClass | scopus | - |
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