Prediction of remaining service life of lithium battery based on VMD-MC-BiLSTM
The growing popularity of battery-powered products, such as electric vehicles and wearable devices, has increasingly motivated the need to predict the remaining life of lithium-based batteries. This study proposes a method for predicting the remaining life of lithium-based batteries based on a hybri...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1459027/full |
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| author | Meng Guangxiong Liang Zhongnan Mou Zhongyi |
| author_facet | Meng Guangxiong Liang Zhongnan Mou Zhongyi |
| author_sort | Meng Guangxiong |
| collection | DOAJ |
| description | The growing popularity of battery-powered products, such as electric vehicles and wearable devices, has increasingly motivated the need to predict the remaining life of lithium-based batteries. This study proposes a method for predicting the remaining life of lithium-based batteries based on a hybrid neural network. First, variational modal decomposition (VMD) was used for noise reduction to maximize retention of the original information and prevent capacity degradation. Second, the trend of capacity decline after noise reduction was modeled and predicted using the combination of bidirectional long short-term memory (BiLSTM) and Monte Carlo (MC) dropout. Finally, experiments were conducted to show that the new method based on the VMD-MC-BiLSTM network achieves good performance for predicting the remaining life of a lithium battery with sufficient confidence level, thereby providing a new approach for optimizing the management of lithium batteries. |
| format | Article |
| id | doaj-art-8bf882f4868f494da9bde2deafb682da |
| institution | OA Journals |
| issn | 2296-598X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-8bf882f4868f494da9bde2deafb682da2025-08-20T02:30:35ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-12-011210.3389/fenrg.2024.14590271459027Prediction of remaining service life of lithium battery based on VMD-MC-BiLSTMMeng Guangxiong0Liang Zhongnan1Mou Zhongyi2Shenhua Group Zhungeer Energy Co., Ltd., Ordos, ChinaQingdao Wohua Soft Control Co., Ltd., Qingdao, ChinaQingdao Wohua Soft Control Co., Ltd., Qingdao, ChinaThe growing popularity of battery-powered products, such as electric vehicles and wearable devices, has increasingly motivated the need to predict the remaining life of lithium-based batteries. This study proposes a method for predicting the remaining life of lithium-based batteries based on a hybrid neural network. First, variational modal decomposition (VMD) was used for noise reduction to maximize retention of the original information and prevent capacity degradation. Second, the trend of capacity decline after noise reduction was modeled and predicted using the combination of bidirectional long short-term memory (BiLSTM) and Monte Carlo (MC) dropout. Finally, experiments were conducted to show that the new method based on the VMD-MC-BiLSTM network achieves good performance for predicting the remaining life of a lithium battery with sufficient confidence level, thereby providing a new approach for optimizing the management of lithium batteries.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1459027/fulllithium batteryremaining useful life predictionVMD-MC-BiLSTM networkdeep learningearly diagnosis |
| spellingShingle | Meng Guangxiong Liang Zhongnan Mou Zhongyi Prediction of remaining service life of lithium battery based on VMD-MC-BiLSTM Frontiers in Energy Research lithium battery remaining useful life prediction VMD-MC-BiLSTM network deep learning early diagnosis |
| title | Prediction of remaining service life of lithium battery based on VMD-MC-BiLSTM |
| title_full | Prediction of remaining service life of lithium battery based on VMD-MC-BiLSTM |
| title_fullStr | Prediction of remaining service life of lithium battery based on VMD-MC-BiLSTM |
| title_full_unstemmed | Prediction of remaining service life of lithium battery based on VMD-MC-BiLSTM |
| title_short | Prediction of remaining service life of lithium battery based on VMD-MC-BiLSTM |
| title_sort | prediction of remaining service life of lithium battery based on vmd mc bilstm |
| topic | lithium battery remaining useful life prediction VMD-MC-BiLSTM network deep learning early diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1459027/full |
| work_keys_str_mv | AT mengguangxiong predictionofremainingservicelifeoflithiumbatterybasedonvmdmcbilstm AT liangzhongnan predictionofremainingservicelifeoflithiumbatterybasedonvmdmcbilstm AT mouzhongyi predictionofremainingservicelifeoflithiumbatterybasedonvmdmcbilstm |