A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks
Lithium-ion battery has been widely used in various fields due to its excellent performance. How to accurately predict its current capacity throughout a battery full lifetime has been a key technology for power system management, assurance, and predictive maintenance. In order to overcome low precis...
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| Format: | Article |
| Language: | English |
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Wiley
2019-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719839637 |
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| _version_ | 1849409966958444544 |
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| author | Pei Wang Xue Dan Yong Yang |
| author_facet | Pei Wang Xue Dan Yong Yang |
| author_sort | Pei Wang |
| collection | DOAJ |
| description | Lithium-ion battery has been widely used in various fields due to its excellent performance. How to accurately predict its current capacity throughout a battery full lifetime has been a key technology for power system management, assurance, and predictive maintenance. In order to overcome low precision problem in long-term prediction for lithium-ion battery capacity, this article proposes a multi-scale fusion prediction method based on ensemble empirical mode decomposition and nonlinear autoregressive models neural networks. The proposed method uses ensemble empirical mode decomposition to decompose the battery capacity measurement sequence to generate multiple intrinsic mode function components on different scales. Then, each component is predicted by nonlinear autoregressive neural networks; finally, the prediction results of each component are reconstructed to obtain the final battery capacity prediction sequence. Experimental results show that the proposed method has higher prediction accuracy and signal adaptability than single nonlinear autoregressive neural networks. |
| format | Article |
| id | doaj-art-7fe0acc07df64f70a80716b7a6fb1f7b |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-7fe0acc07df64f70a80716b7a6fb1f7b2025-08-20T03:35:19ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-03-011510.1177/1550147719839637A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networksPei Wang0Xue Dan1Yong Yang2National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, ChinaNational Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, ChinaState Key Laboratory of Solidification Processing, Center of Advanced Lubrication and Seal Materials, Northwestern Polytechnical University, Xi’an, ChinaLithium-ion battery has been widely used in various fields due to its excellent performance. How to accurately predict its current capacity throughout a battery full lifetime has been a key technology for power system management, assurance, and predictive maintenance. In order to overcome low precision problem in long-term prediction for lithium-ion battery capacity, this article proposes a multi-scale fusion prediction method based on ensemble empirical mode decomposition and nonlinear autoregressive models neural networks. The proposed method uses ensemble empirical mode decomposition to decompose the battery capacity measurement sequence to generate multiple intrinsic mode function components on different scales. Then, each component is predicted by nonlinear autoregressive neural networks; finally, the prediction results of each component are reconstructed to obtain the final battery capacity prediction sequence. Experimental results show that the proposed method has higher prediction accuracy and signal adaptability than single nonlinear autoregressive neural networks.https://doi.org/10.1177/1550147719839637 |
| spellingShingle | Pei Wang Xue Dan Yong Yang A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks International Journal of Distributed Sensor Networks |
| title | A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks |
| title_full | A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks |
| title_fullStr | A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks |
| title_full_unstemmed | A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks |
| title_short | A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks |
| title_sort | multi scale fusion prediction method for lithium ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks |
| url | https://doi.org/10.1177/1550147719839637 |
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