Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect
Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/9/1685 |
| _version_ | 1828120183951065088 |
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| author | Xiaodong Xu Chuanqiang Yu Shengjin Tang Xiaoyan Sun Xiaosheng Si Lifeng Wu |
| author_facet | Xiaodong Xu Chuanqiang Yu Shengjin Tang Xiaoyan Sun Xiaosheng Si Lifeng Wu |
| author_sort | Xiaodong Xu |
| collection | DOAJ |
| description | Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value. |
| first_indexed | 2024-04-11T14:01:10Z |
| format | Article |
| id | doaj.art-17c28d7da9fa42f18dc7ac3c447ff6e0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T14:01:10Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-17c28d7da9fa42f18dc7ac3c447ff6e02022-12-22T04:20:07ZengMDPI AGEnergies1996-10732019-05-01129168510.3390/en12091685en12091685Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation EffectXiaodong Xu0Chuanqiang Yu1Shengjin Tang2Xiaoyan Sun3Xiaosheng Si4Lifeng Wu5High-Tech Institute of Xi’an, Xi’an 710025, ChinaHigh-Tech Institute of Xi’an, Xi’an 710025, ChinaHigh-Tech Institute of Xi’an, Xi’an 710025, ChinaHigh-Tech Institute of Xi’an, Xi’an 710025, ChinaHigh-Tech Institute of Xi’an, Xi’an 710025, ChinaCollege of Information Engineering, Capital Normal University, Beijing 100048, ChinaRemaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.https://www.mdpi.com/1996-1073/12/9/1685lithium-ion batteryrelaxationremaining useful liferegenerated useful timeWiener processesBayesian frameworkmaximum likelihood estimation |
| spellingShingle | Xiaodong Xu Chuanqiang Yu Shengjin Tang Xiaoyan Sun Xiaosheng Si Lifeng Wu Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect Energies lithium-ion battery relaxation remaining useful life regenerated useful time Wiener processes Bayesian framework maximum likelihood estimation |
| title | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect |
| title_full | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect |
| title_fullStr | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect |
| title_full_unstemmed | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect |
| title_short | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect |
| title_sort | remaining useful life prediction of lithium ion batteries based on wiener processes with considering the relaxation effect |
| topic | lithium-ion battery relaxation remaining useful life regenerated useful time Wiener processes Bayesian framework maximum likelihood estimation |
| url | https://www.mdpi.com/1996-1073/12/9/1685 |
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