An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD
The signals of lithium-ion battery degradation are non-stationary and nonlinear. To adaptively extract the health indicator(HI) that can accurately represent the battery degradation characters and improve the prediction precision of battery remaining useful life (RUL), a stacked auto encoder-variati...
| Format: | Article |
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| Language: | zho |
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EDP Sciences
2020-08-01
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| Series: | Xibei Gongye Daxue Xuebao |
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| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2020/04/jnwpu2020384p814/jnwpu2020384p814.html |
| _version_ | 1797668887355981824 |
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| collection | DOAJ |
| description | The signals of lithium-ion battery degradation are non-stationary and nonlinear. To adaptively extract the health indicator(HI) that can accurately represent the battery degradation characters and improve the prediction precision of battery remaining useful life (RUL), a stacked auto encoder-variational mode decomposition(SAE-VMD) based HI construction framework is proposed. Firstly, the stacked auto encoder(SAE) is used to reduce the noises of battery parameters and lower the data dimensionality and construct a syncretic HI that contains the battery degradation characters. Then the variational mode decomposition(VMD) is employed for effectively separating the syncretic HI into three modalities: the global attenuation, the local regeneration and the noises. The three modalities are selected as HIs to eliminate the HI noises and improve the RUL prediction precision. The RUL prediction results of lithium-ion battery indicate that the HI extracted by using the present method can obtain a better RUL prediction precision and verify the high quality of the extracted HI. |
| first_indexed | 2024-03-11T20:36:03Z |
| format | Article |
| id | doaj.art-30aac487a44c49ff9c91ad457cbc88cf |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-11T20:36:03Z |
| publishDate | 2020-08-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-30aac487a44c49ff9c91ad457cbc88cf2023-10-02T06:06:55ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252020-08-0138481482110.1051/jnwpu/20203840814jnwpu2020384p814An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD012School of Computer Science and Engineering, Northwestern Polytechnical UniversitySchool of Computer Science and Engineering, Northwestern Polytechnical UniversitySchool of Computer Science and Engineering, Northwestern Polytechnical UniversityThe signals of lithium-ion battery degradation are non-stationary and nonlinear. To adaptively extract the health indicator(HI) that can accurately represent the battery degradation characters and improve the prediction precision of battery remaining useful life (RUL), a stacked auto encoder-variational mode decomposition(SAE-VMD) based HI construction framework is proposed. Firstly, the stacked auto encoder(SAE) is used to reduce the noises of battery parameters and lower the data dimensionality and construct a syncretic HI that contains the battery degradation characters. Then the variational mode decomposition(VMD) is employed for effectively separating the syncretic HI into three modalities: the global attenuation, the local regeneration and the noises. The three modalities are selected as HIs to eliminate the HI noises and improve the RUL prediction precision. The RUL prediction results of lithium-ion battery indicate that the HI extracted by using the present method can obtain a better RUL prediction precision and verify the high quality of the extracted HI.https://www.jnwpu.org/articles/jnwpu/full_html/2020/04/jnwpu2020384p814/jnwpu2020384p814.htmllithium-ion batteryremaining useful lifehealth indicatorstacked auto encodervariational mode decomposition |
| spellingShingle | An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD Xibei Gongye Daxue Xuebao lithium-ion battery remaining useful life health indicator stacked auto encoder variational mode decomposition |
| title | An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD |
| title_full | An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD |
| title_fullStr | An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD |
| title_full_unstemmed | An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD |
| title_short | An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD |
| title_sort | hi extraction framework for lithium ion battery prognostics based on sae vmd |
| topic | lithium-ion battery remaining useful life health indicator stacked auto encoder variational mode decomposition |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2020/04/jnwpu2020384p814/jnwpu2020384p814.html |