Variational mode decomposition enabled temporal convolutional network model for state of charge estimation
Abstract Due to the fast growth of electric vehicles (EVs) , estimation for Battery's State‐of‐charge (SOC) received significant research interests. The reason is that an accurate SOC estimation can significantly contribute to the reliability of EVs. A Variational Mode Decomposition (VMD) techn...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-09-01
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| Series: | IET Cyber-Physical Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cps2.12053 |
| _version_ | 1797689901044465664 |
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| author | Zhaocheng Zhang Tao Cai Aote Yuan |
| author_facet | Zhaocheng Zhang Tao Cai Aote Yuan |
| author_sort | Zhaocheng Zhang |
| collection | DOAJ |
| description | Abstract Due to the fast growth of electric vehicles (EVs) , estimation for Battery's State‐of‐charge (SOC) received significant research interests. The reason is that an accurate SOC estimation can significantly contribute to the reliability of EVs. A Variational Mode Decomposition (VMD) technique enabled Temporal Convolutional Network (TCN) model is proposed by the authors for SOC estimation. The proposed method first adopts time‐frequency analysis techniques to decompose voltage values into different frequency domains, each of which is analysed with the VMD technique to obtain its features as the input for the TCN model. Then, the proposed method combines outputs of different frequency domains with an attention module as the final output of the TCN model. Experiments on real battery datasets indicate that the proposed method outperforms the existing methods by 7.2% in mean absolute error and 6.13% in root mean square error. In addition, the error between the estimated and actual values using the proposed method is bounded by 2%. |
| first_indexed | 2024-03-12T01:51:56Z |
| format | Article |
| id | doaj.art-0ab60dd4b9d4484290a89810bc043400 |
| institution | Directory Open Access Journal |
| issn | 2398-3396 |
| language | English |
| last_indexed | 2024-03-12T01:51:56Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Cyber-Physical Systems |
| spelling | doaj.art-0ab60dd4b9d4484290a89810bc0434002023-09-08T09:04:19ZengWileyIET Cyber-Physical Systems2398-33962023-09-018319520410.1049/cps2.12053Variational mode decomposition enabled temporal convolutional network model for state of charge estimationZhaocheng Zhang0Tao Cai1Aote Yuan2School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan Hubei ChinaSchool of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan Hubei ChinaSchool of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan Hubei ChinaAbstract Due to the fast growth of electric vehicles (EVs) , estimation for Battery's State‐of‐charge (SOC) received significant research interests. The reason is that an accurate SOC estimation can significantly contribute to the reliability of EVs. A Variational Mode Decomposition (VMD) technique enabled Temporal Convolutional Network (TCN) model is proposed by the authors for SOC estimation. The proposed method first adopts time‐frequency analysis techniques to decompose voltage values into different frequency domains, each of which is analysed with the VMD technique to obtain its features as the input for the TCN model. Then, the proposed method combines outputs of different frequency domains with an attention module as the final output of the TCN model. Experiments on real battery datasets indicate that the proposed method outperforms the existing methods by 7.2% in mean absolute error and 6.13% in root mean square error. In addition, the error between the estimated and actual values using the proposed method is bounded by 2%.https://doi.org/10.1049/cps2.12053battery management systemsneural nets |
| spellingShingle | Zhaocheng Zhang Tao Cai Aote Yuan Variational mode decomposition enabled temporal convolutional network model for state of charge estimation IET Cyber-Physical Systems battery management systems neural nets |
| title | Variational mode decomposition enabled temporal convolutional network model for state of charge estimation |
| title_full | Variational mode decomposition enabled temporal convolutional network model for state of charge estimation |
| title_fullStr | Variational mode decomposition enabled temporal convolutional network model for state of charge estimation |
| title_full_unstemmed | Variational mode decomposition enabled temporal convolutional network model for state of charge estimation |
| title_short | Variational mode decomposition enabled temporal convolutional network model for state of charge estimation |
| title_sort | variational mode decomposition enabled temporal convolutional network model for state of charge estimation |
| topic | battery management systems neural nets |
| url | https://doi.org/10.1049/cps2.12053 |
| work_keys_str_mv | AT zhaochengzhang variationalmodedecompositionenabledtemporalconvolutionalnetworkmodelforstateofchargeestimation AT taocai variationalmodedecompositionenabledtemporalconvolutionalnetworkmodelforstateofchargeestimation AT aoteyuan variationalmodedecompositionenabledtemporalconvolutionalnetworkmodelforstateofchargeestimation |