An ensemble model for predicting the remaining useful performance of lithium-ion batteries
► The capacity degradation of lithium battery was characterized by an ensemble model. ► The remaining useful performance was presented as probability distribution. ► The robustness of the algorithm was verified by different datasets. ► More measured data, more accurate prediction results. We develop...
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| Published in | Microelectronics and reliability Vol. 53; no. 6; pp. 811 - 820 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.06.2013
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| Subjects | |
| Online Access | Get full text |
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| Abstract | ► The capacity degradation of lithium battery was characterized by an ensemble model. ► The remaining useful performance was presented as probability distribution. ► The robustness of the algorithm was verified by different datasets. ► More measured data, more accurate prediction results.
We developed an ensemble model to characterize the capacity degradation and predict the remaining useful performance (RUP) of lithium-ion batteries. Our model fuses an empirical exponential and a polynomial regression model to track the battery’s degradation trend over its cycle life based on experimental data analysis. Model parameters are adjusted online using a particle filtering (PF) approach. Experiments were conducted to compare our ensemble model’s prediction performance with the individual results of the exponential and polynomial models. A validation set of experimental battery capacity data was used to evaluate our model. In our conclusion, we presented the limitations of our model. |
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| AbstractList | We developed an ensemble model to characterize the capacity degradation and predict the remaining useful performance (RUP) of lithium-ion batteries. Our model fuses an empirical exponential and a polynomial regression model to track the batteryas degradation trend over its cycle life based on experimental data analysis. Model parameters are adjusted online using a particle filtering (PF) approach. Experiments were conducted to compare our ensemble modelas prediction performance with the individual results of the exponential and polynomial models. A validation set of experimental battery capacity data was used to evaluate our model. In our conclusion, we presented the limitations of our model. ► The capacity degradation of lithium battery was characterized by an ensemble model. ► The remaining useful performance was presented as probability distribution. ► The robustness of the algorithm was verified by different datasets. ► More measured data, more accurate prediction results. We developed an ensemble model to characterize the capacity degradation and predict the remaining useful performance (RUP) of lithium-ion batteries. Our model fuses an empirical exponential and a polynomial regression model to track the battery’s degradation trend over its cycle life based on experimental data analysis. Model parameters are adjusted online using a particle filtering (PF) approach. Experiments were conducted to compare our ensemble model’s prediction performance with the individual results of the exponential and polynomial models. A validation set of experimental battery capacity data was used to evaluate our model. In our conclusion, we presented the limitations of our model. |
| Author | Ma, Eden W.M. Xing, Yinjiao Pecht, Michael Tsui, Kwok-Leung |
| Author_xml | – sequence: 1 givenname: Yinjiao surname: Xing fullname: Xing, Yinjiao email: yxing3@student.cityu.edu.hk organization: Centre for Prognostics and System Health Management, City University of Hong Kong, Kowloon, Hong Kong – sequence: 2 givenname: Eden W.M. surname: Ma fullname: Ma, Eden W.M. email: eden.wm.ma@cityu.edu.hk organization: Centre for Prognostics and System Health Management, City University of Hong Kong, Kowloon, Hong Kong – sequence: 3 givenname: Kwok-Leung surname: Tsui fullname: Tsui, Kwok-Leung email: kltsui@cityu.edu.hk organization: Centre for Prognostics and System Health Management, City University of Hong Kong, Kowloon, Hong Kong – sequence: 4 givenname: Michael surname: Pecht fullname: Pecht, Michael email: pecht@calce.umd.edu organization: Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20740, USA |
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| Snippet | ► The capacity degradation of lithium battery was characterized by an ensemble model. ► The remaining useful performance was presented as probability... We developed an ensemble model to characterize the capacity degradation and predict the remaining useful performance (RUP) of lithium-ion batteries. Our model... |
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| Title | An ensemble model for predicting the remaining useful performance of lithium-ion batteries |
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