Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm
Abstract Accurate prediction of lithium-ion batteries’ remaining useful life (RUL) is critical for system reliability and safety. This study proposes a novel forecasting framework that fuses modal decomposition with the advanced PatchTST model. Initially, the Spearman correlation coefficient is empl...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11934-7 |
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| author | Pei Tang Zetao Qiu Zhongran Yao Jiahao Pan Dashuai Cheng Xiaoyong Gu Changcheng Sun |
| author_facet | Pei Tang Zetao Qiu Zhongran Yao Jiahao Pan Dashuai Cheng Xiaoyong Gu Changcheng Sun |
| author_sort | Pei Tang |
| collection | DOAJ |
| description | Abstract Accurate prediction of lithium-ion batteries’ remaining useful life (RUL) is critical for system reliability and safety. This study proposes a novel forecasting framework that fuses modal decomposition with the advanced PatchTST model. Initially, the Spearman correlation coefficient is employed to identify features strongly associated with battery capacity. The Variational Mode Decomposition (VMD) method is then used to break down the raw capacity sequence into a set of intrinsic mode functions. To enhance decomposition quality, the Whale Optimization Algorithm (WOA) optimizes the number of modes K and penalty factor α by minimizing mean envelope entropy. The selected features and decomposed components are subsequently input into a PatchTST network, whose hyperparameters are tuned via the Sparrow Search Algorithm (SSA), to predict battery RUL. Experimental validation on the NASA Battery dataset and NASA Randomized Battery Usage Dataset demonstrates that the proposed WOA-VMD-SSA-PatchTST model consistently outperforms baseline models, including CNN, GRU and PatchTST, achieving superior prediction accuracy and robustness. |
| format | Article |
| id | doaj-art-c7775e8bb1344f48886dd1c243cfd033 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c7775e8bb1344f48886dd1c243cfd0332025-08-20T04:01:51ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-11934-7Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithmPei Tang0Zetao Qiu1Zhongran Yao2Jiahao Pan3Dashuai Cheng4Xiaoyong Gu5Changcheng Sun6School of Automotive Engineering, Yancheng Institute of TechnologySchool of Automotive Engineering, Yancheng Institute of TechnologySchool of Automobile and Traffic Engineering, Wuxi Institute of TechnologySchool of Automotive Engineering, Yancheng Institute of TechnologySchool of Automotive Engineering, Yancheng Institute of TechnologyEngineering Research Center of New Energy Vehicle Energy Saving and Battery Safety, Wuxi Institute of TechnologySchool of Automotive Engineering, Yancheng Institute of TechnologyAbstract Accurate prediction of lithium-ion batteries’ remaining useful life (RUL) is critical for system reliability and safety. This study proposes a novel forecasting framework that fuses modal decomposition with the advanced PatchTST model. Initially, the Spearman correlation coefficient is employed to identify features strongly associated with battery capacity. The Variational Mode Decomposition (VMD) method is then used to break down the raw capacity sequence into a set of intrinsic mode functions. To enhance decomposition quality, the Whale Optimization Algorithm (WOA) optimizes the number of modes K and penalty factor α by minimizing mean envelope entropy. The selected features and decomposed components are subsequently input into a PatchTST network, whose hyperparameters are tuned via the Sparrow Search Algorithm (SSA), to predict battery RUL. Experimental validation on the NASA Battery dataset and NASA Randomized Battery Usage Dataset demonstrates that the proposed WOA-VMD-SSA-PatchTST model consistently outperforms baseline models, including CNN, GRU and PatchTST, achieving superior prediction accuracy and robustness.https://doi.org/10.1038/s41598-025-11934-7Lithium-ion batteryRemaining useful lifeVariational modal decompositionPatch time series transformer |
| spellingShingle | Pei Tang Zetao Qiu Zhongran Yao Jiahao Pan Dashuai Cheng Xiaoyong Gu Changcheng Sun Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm Scientific Reports Lithium-ion battery Remaining useful life Variational modal decomposition Patch time series transformer |
| title | Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm |
| title_full | Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm |
| title_fullStr | Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm |
| title_full_unstemmed | Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm |
| title_short | Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm |
| title_sort | lithium ion battery rul prediction based on optimized vmd ssa patchtst algorithm |
| topic | Lithium-ion battery Remaining useful life Variational modal decomposition Patch time series transformer |
| url | https://doi.org/10.1038/s41598-025-11934-7 |
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