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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| Published in | Electronics (Basel) Vol. 10; no. 5; p. 540 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
01.03.2021
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| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Yang, Chenglin Wang, Jingyuan Zhou, Xiuyun Liu, Zhen Long, Bing Chen, Xiaowu |
| Author_xml | – sequence: 1 givenname: Xiaowu orcidid: 0000-0002-6673-9225 surname: Chen fullname: Chen, Xiaowu – sequence: 2 givenname: Zhen orcidid: 0000-0003-3406-0039 surname: Liu fullname: Liu, Zhen – sequence: 3 givenname: Jingyuan surname: Wang fullname: Wang, Jingyuan – sequence: 4 givenname: Chenglin surname: Yang fullname: Yang, Chenglin – sequence: 5 givenname: Bing surname: Long fullname: Long, Bing – sequence: 6 givenname: Xiuyun surname: Zhou fullname: Zhou, Xiuyun |
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