State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture
State-of-charge (SOC) estimation of lithium-ion batteries based on deep learning techniques has been receiving considerable attention. However, most deep-learning-based methods focus on SOC estimation at fixed ambient temperatures and cannot provide useful indications for battery state in real-world...
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| Vydané v: | Journal of power sources Ročník 449; s. 227558 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
15.02.2020
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| ISSN: | 0378-7753, 1873-2755 |
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| Abstract | State-of-charge (SOC) estimation of lithium-ion batteries based on deep learning techniques has been receiving considerable attention. However, most deep-learning-based methods focus on SOC estimation at fixed ambient temperatures and cannot provide useful indications for battery state in real-world scenarios because batteries usually experience varying temperatures during operation. In this study, an encoder-decoder with bidirectional long short-term memory (LSTM) is proposed for estimating the SOC at different temperature conditions. This end-to-end model can learn sequential information from the measurement sequences to characterize battery dynamics for sequence estimation. Introducing the bidirectional LSTMs into the encoder-decoder enables the model to capture the long-term dependencies of the measurement sequences from both past and future directions to increase the estimation accuracy. The proposed method is evaluated on public battery datasets under dynamic loading profiles. Validation with an experimental dataset shows that this method of considering the sequential contexts and bidirectional dependencies of battery measurement data can accurately estimate the SOC at different ambient temperatures. In particular, the mean absolute errors are as low as 1.07% at varying temperatures. The proposed method can improve the reliability and availability of battery management systems for monitoring the battery state under varying ambient conditions.
•State-of-charge sequence estimation based on encoder-decoder architecture.•Comprehensively learns sequential contexts from battery measured data.•Bidirectional long short-term memory captures temporal dependencies of battery.•Estimate state-of-charge sequences at different ambient temperatures. |
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| AbstractList | State-of-charge (SOC) estimation of lithium-ion batteries based on deep learning techniques has been receiving considerable attention. However, most deep-learning-based methods focus on SOC estimation at fixed ambient temperatures and cannot provide useful indications for battery state in real-world scenarios because batteries usually experience varying temperatures during operation. In this study, an encoder-decoder with bidirectional long short-term memory (LSTM) is proposed for estimating the SOC at different temperature conditions. This end-to-end model can learn sequential information from the measurement sequences to characterize battery dynamics for sequence estimation. Introducing the bidirectional LSTMs into the encoder-decoder enables the model to capture the long-term dependencies of the measurement sequences from both past and future directions to increase the estimation accuracy. The proposed method is evaluated on public battery datasets under dynamic loading profiles. Validation with an experimental dataset shows that this method of considering the sequential contexts and bidirectional dependencies of battery measurement data can accurately estimate the SOC at different ambient temperatures. In particular, the mean absolute errors are as low as 1.07% at varying temperatures. The proposed method can improve the reliability and availability of battery management systems for monitoring the battery state under varying ambient conditions.
•State-of-charge sequence estimation based on encoder-decoder architecture.•Comprehensively learns sequential contexts from battery measured data.•Bidirectional long short-term memory captures temporal dependencies of battery.•Estimate state-of-charge sequences at different ambient temperatures. |
| ArticleNumber | 227558 |
| Author | Bian, Chong Yang, Shunkun Huang, Tingting He, Huoliang |
| Author_xml | – sequence: 1 givenname: Chong surname: Bian fullname: Bian, Chong organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China – sequence: 2 givenname: Huoliang surname: He fullname: He, Huoliang organization: School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China – sequence: 3 givenname: Shunkun surname: Yang fullname: Yang, Shunkun email: ysk@buaa.edu.cn organization: School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China – sequence: 4 givenname: Tingting surname: Huang fullname: Huang, Tingting organization: School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China |
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| Keywords | Lithium-ion battery State-of-charge sequence estimation Encoder-decoder Bidirectional long short-term memory |
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