Remaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey model
Abstract Remaining useful life (RUL) prediction is a crucial aspect of the prognostics health management of lithium-ion batteries (LIBs). Owing to the influence of resampling technology, particle degradation is often observed in the particle filter-based RUL prediction of LIBs, resulting in a low pr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86511-z |
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| author | Wang Shuai Li Yiting Zhou Shoubin Chen Lifei Michael Pecht |
| author_facet | Wang Shuai Li Yiting Zhou Shoubin Chen Lifei Michael Pecht |
| author_sort | Wang Shuai |
| collection | DOAJ |
| description | Abstract Remaining useful life (RUL) prediction is a crucial aspect of the prognostics health management of lithium-ion batteries (LIBs). Owing to the influence of resampling technology, particle degradation is often observed in the particle filter-based RUL prediction of LIBs, resulting in a low prediction accuracy and large uncertainty. In this paper, a novel particle flow filter with the grey model method (GM-PFF) is proposed to forecast the RUL and state of health of batteries. First, the least squares method is employed to obtain the initial values for double exponential empirical model parameters. Subsequently, the grey model is used to predict the current cycle capacity of LIBs as an observation value for the particle flow filter, solving the inaccurate estimation problem of the state of particle flow filter observation values, and the particle flow filter method is employed to update model parameters. Finally, a test dataset is divided into early, middle, and late stages to predict the RUL of LIBs and obtain the probability distributions. On the CALCE and NASA PCoE LIB dataset, GM-PFF reduces RMSE by 1% compared to PFF, exhibiting a higher prediction accuracy and effectively addressing the particle degradation problem. |
| format | Article |
| id | doaj-art-77ca436bf90946db94a2d1ece14a80a3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-77ca436bf90946db94a2d1ece14a80a32025-02-02T12:18:45ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-86511-zRemaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey modelWang Shuai0Li Yiting1Zhou Shoubin2Chen Lifei3Michael Pecht4Digital Fujian Internet-of-Things Laboatory of Environmental Monitoring, College of Computer and Cyber Security, Fujian Normal UniversityDigital Fujian Internet-of-Things Laboatory of Environmental Monitoring, College of Computer and Cyber Security, Fujian Normal UniversityHuafu Hight Technology Energy Storage CoDigital Fujian Internet-of-Things Laboatory of Environmental Monitoring, College of Computer and Cyber Security, Fujian Normal UniversityCALCE University of MarylandAbstract Remaining useful life (RUL) prediction is a crucial aspect of the prognostics health management of lithium-ion batteries (LIBs). Owing to the influence of resampling technology, particle degradation is often observed in the particle filter-based RUL prediction of LIBs, resulting in a low prediction accuracy and large uncertainty. In this paper, a novel particle flow filter with the grey model method (GM-PFF) is proposed to forecast the RUL and state of health of batteries. First, the least squares method is employed to obtain the initial values for double exponential empirical model parameters. Subsequently, the grey model is used to predict the current cycle capacity of LIBs as an observation value for the particle flow filter, solving the inaccurate estimation problem of the state of particle flow filter observation values, and the particle flow filter method is employed to update model parameters. Finally, a test dataset is divided into early, middle, and late stages to predict the RUL of LIBs and obtain the probability distributions. On the CALCE and NASA PCoE LIB dataset, GM-PFF reduces RMSE by 1% compared to PFF, exhibiting a higher prediction accuracy and effectively addressing the particle degradation problem.https://doi.org/10.1038/s41598-025-86511-zLithium-ion batteriesRemaining useful lifeParticle flow filterParticle filter |
| spellingShingle | Wang Shuai Li Yiting Zhou Shoubin Chen Lifei Michael Pecht Remaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey model Scientific Reports Lithium-ion batteries Remaining useful life Particle flow filter Particle filter |
| title | Remaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey model |
| title_full | Remaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey model |
| title_fullStr | Remaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey model |
| title_full_unstemmed | Remaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey model |
| title_short | Remaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey model |
| title_sort | remaining useful life prediction of lithium ion batteries using a novel particle flow filter framework with grey model |
| topic | Lithium-ion batteries Remaining useful life Particle flow filter Particle filter |
| url | https://doi.org/10.1038/s41598-025-86511-z |
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