Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error
Remaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distrib...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-01-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/7/2/520 |
| _version_ | 1811304006372294656 |
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| author | Shengjin Tang Chuanqiang Yu Xue Wang Xiaosong Guo Xiaosheng Si |
| author_facet | Shengjin Tang Chuanqiang Yu Xue Wang Xiaosong Guo Xiaosheng Si |
| author_sort | Shengjin Tang |
| collection | DOAJ |
| description | Remaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distribution (TND) based modeling approach for the estimated degradation state and obtain an exact and closed-form RUL distribution by simultaneously considering the measurement uncertainty and the distribution of the estimated drift parameter. Then, the traditional maximum likelihood estimation (MLE) method for population based parameters estimation is remedied to improve the estimation efficiency. Additionally, we analyze the relationship between the classic MLE method and the combination of the Bayesian updating algorithm and the expectation maximization algorithm for the real time RUL prediction. Interestingly, it is found that the result of the combination algorithm is equal to the classic MLE method. Inspired by this observation, a heuristic algorithm for the real time parameters updating is presented. Finally, numerical examples and a case study of lithium-ion batteries are provided to substantiate the superiority of the proposed RUL prediction method. |
| first_indexed | 2024-04-13T07:59:18Z |
| format | Article |
| id | doaj.art-9543a432b6e9493f81c74a68b144ff78 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T07:59:18Z |
| publishDate | 2014-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9543a432b6e9493f81c74a68b144ff782022-12-22T02:55:20ZengMDPI AGEnergies1996-10732014-01-017252054710.3390/en7020520en7020520Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement ErrorShengjin Tang0Chuanqiang Yu1Xue Wang2Xiaosong Guo3Xiaosheng Si4High-Tech Institute of Xi'an, Xi'an, Shaanxi 710025, ChinaHigh-Tech Institute of Xi'an, Xi'an, Shaanxi 710025, ChinaDepartment of Precision Instrument, Tsinghua University, Beijing 100084, ChinaHigh-Tech Institute of Xi'an, Xi'an, Shaanxi 710025, ChinaHigh-Tech Institute of Xi'an, Xi'an, Shaanxi 710025, ChinaRemaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distribution (TND) based modeling approach for the estimated degradation state and obtain an exact and closed-form RUL distribution by simultaneously considering the measurement uncertainty and the distribution of the estimated drift parameter. Then, the traditional maximum likelihood estimation (MLE) method for population based parameters estimation is remedied to improve the estimation efficiency. Additionally, we analyze the relationship between the classic MLE method and the combination of the Bayesian updating algorithm and the expectation maximization algorithm for the real time RUL prediction. Interestingly, it is found that the result of the combination algorithm is equal to the classic MLE method. Inspired by this observation, a heuristic algorithm for the real time parameters updating is presented. Finally, numerical examples and a case study of lithium-ion batteries are provided to substantiate the superiority of the proposed RUL prediction method.http://www.mdpi.com/1996-1073/7/2/520lithium-ion batteriesremaining useful lifethe Wiener processmeasurement errorpredictiontruncated normal distributionmaximum likelihood estimationBayesianexpectation maximization algorithm |
| spellingShingle | Shengjin Tang Chuanqiang Yu Xue Wang Xiaosong Guo Xiaosheng Si Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error Energies lithium-ion batteries remaining useful life the Wiener process measurement error prediction truncated normal distribution maximum likelihood estimation Bayesian expectation maximization algorithm |
| title | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error |
| title_full | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error |
| title_fullStr | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error |
| title_full_unstemmed | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error |
| title_short | Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error |
| title_sort | remaining useful life prediction of lithium ion batteries based on the wiener process with measurement error |
| topic | lithium-ion batteries remaining useful life the Wiener process measurement error prediction truncated normal distribution maximum likelihood estimation Bayesian expectation maximization algorithm |
| url | http://www.mdpi.com/1996-1073/7/2/520 |
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