An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine
Lithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consider the randomness of the changing point in the tw...
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MDPI AG
2021-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/5/540 |
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| author | Xiaowu Chen Zhen Liu Jingyuan Wang Chenglin Yang Bing Long Xiuyun Zhou |
| author_facet | Xiaowu Chen Zhen Liu Jingyuan Wang Chenglin Yang Bing Long Xiuyun Zhou |
| author_sort | Xiaowu Chen |
| collection | DOAJ |
| description | Lithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consider the randomness of the changing point in the two-phase model and cannot update the change time in real time. Therefore, this paper proposes a method based on the combination of the two-phase Wiener model and an extreme learning machine (ELM). The two-phase Wiener model is used to derive the mathematical expression of the remaining useful life (RUL), and the ELM is implemented to adaptively detect the changing point. Based on the Poisson distribution, the distribution of the changing time is derived as a gamma distribution. To evaluate the theoretical results and practicality of the proposed method, we perform both numerical and practical simulations. The results of the simulations show that due to the precise and adaptive detection of changing points, the proposed method produces a more accurate RUL prediction than existing methods. The error of our method for detecting the changing point is about 4% and the mean prediction error of RUL in the second phase is improved from 4.39 cycles to 1.61 cycles. |
| first_indexed | 2024-03-09T00:34:06Z |
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| id | doaj.art-f3a54ea35c9e4a4caade904df0d4dbde |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T00:34:06Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-f3a54ea35c9e4a4caade904df0d4dbde2023-12-11T18:20:34ZengMDPI AGElectronics2079-92922021-02-0110554010.3390/electronics10050540An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning MachineXiaowu Chen0Zhen Liu1Jingyuan Wang2Chenglin Yang3Bing Long4Xiuyun Zhou5School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaLithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consider the randomness of the changing point in the two-phase model and cannot update the change time in real time. Therefore, this paper proposes a method based on the combination of the two-phase Wiener model and an extreme learning machine (ELM). The two-phase Wiener model is used to derive the mathematical expression of the remaining useful life (RUL), and the ELM is implemented to adaptively detect the changing point. Based on the Poisson distribution, the distribution of the changing time is derived as a gamma distribution. To evaluate the theoretical results and practicality of the proposed method, we perform both numerical and practical simulations. The results of the simulations show that due to the precise and adaptive detection of changing points, the proposed method produces a more accurate RUL prediction than existing methods. The error of our method for detecting the changing point is about 4% and the mean prediction error of RUL in the second phase is improved from 4.39 cycles to 1.61 cycles.https://www.mdpi.com/2079-9292/10/5/540lithium-ion batterytwo-phase wiener process modelextreme learning machineadaptive detection of changing pointsremaining useful life prediction |
| spellingShingle | Xiaowu Chen Zhen Liu Jingyuan Wang Chenglin Yang Bing Long Xiuyun Zhou An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine Electronics lithium-ion battery two-phase wiener process model extreme learning machine adaptive detection of changing points remaining useful life prediction |
| title | An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine |
| title_full | An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine |
| title_fullStr | An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine |
| title_full_unstemmed | An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine |
| title_short | An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine |
| title_sort | adaptive prediction model for the remaining life of an li ion battery based on the fusion of the two phase wiener process and an extreme learning machine |
| topic | lithium-ion battery two-phase wiener process model extreme learning machine adaptive detection of changing points remaining useful life prediction |
| url | https://www.mdpi.com/2079-9292/10/5/540 |
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