An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine
The early detection of defective lithium-ion batteries in cellular phones is critical due to the rapid increase in popularity and mass production of cellular phones. It is essential for manufacturers to design an optimal burn-in policy to differentiate between normal and weak batteries in short cycl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/11/11/3021 |
| _version_ | 1811300216779833344 |
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| author | Jinsong Yu Jie Yang Diyin Tang Jing Dai |
| author_facet | Jinsong Yu Jie Yang Diyin Tang Jing Dai |
| author_sort | Jinsong Yu |
| collection | DOAJ |
| description | The early detection of defective lithium-ion batteries in cellular phones is critical due to the rapid increase in popularity and mass production of cellular phones. It is essential for manufacturers to design an optimal burn-in policy to differentiate between normal and weak batteries in short cycles prior to shipping them to the marketplace. A novel approach to determine the optimal burn-in policy using a feature selection strategy and relevance vector machine (RVM) is proposed. The sequential floating forward search (SFFS) is used as the feature selection method to find an optimal feature subset from the entire sequence of the batteries’ quality characteristics while preserving the original variables. Given the selected feature subset, the RVM is applied to classify batteries into two groups and simultaneously obtain the posterior probabilities. To achieve better discrimination performance with less risk, a new characteristic is extracted from the discharge profile. Subsequently, an optimization cost model is developed by introducing a classification instability penalty to ensure the stability of the optimal number of burn-in cycles. A case study utilizing cellular phone lithium-ion batteries randomly selected from manufactured lots is presented to illustrate the proposed methodology. Furthermore, we conduct a comparison with the cumulative degradation (CD) method and non-cumulative degradation (NCD) method based on the Wiener process. The results show that our proposed burn-in test method performs better than comparable methods. |
| first_indexed | 2024-04-13T06:47:52Z |
| format | Article |
| id | doaj.art-aa3fe7bc85e241aca212eba7c343977a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T06:47:52Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-aa3fe7bc85e241aca212eba7c343977a2022-12-22T02:57:32ZengMDPI AGEnergies1996-10732018-11-011111302110.3390/en11113021en11113021An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector MachineJinsong Yu0Jie Yang1Diyin Tang2Jing Dai3School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaChina Academy of Launch Vehicle Technology R&D Center, No. 1 Nan Da Hong Men Road, FengTai District, Beijing 100076, ChinaThe early detection of defective lithium-ion batteries in cellular phones is critical due to the rapid increase in popularity and mass production of cellular phones. It is essential for manufacturers to design an optimal burn-in policy to differentiate between normal and weak batteries in short cycles prior to shipping them to the marketplace. A novel approach to determine the optimal burn-in policy using a feature selection strategy and relevance vector machine (RVM) is proposed. The sequential floating forward search (SFFS) is used as the feature selection method to find an optimal feature subset from the entire sequence of the batteries’ quality characteristics while preserving the original variables. Given the selected feature subset, the RVM is applied to classify batteries into two groups and simultaneously obtain the posterior probabilities. To achieve better discrimination performance with less risk, a new characteristic is extracted from the discharge profile. Subsequently, an optimization cost model is developed by introducing a classification instability penalty to ensure the stability of the optimal number of burn-in cycles. A case study utilizing cellular phone lithium-ion batteries randomly selected from manufactured lots is presented to illustrate the proposed methodology. Furthermore, we conduct a comparison with the cumulative degradation (CD) method and non-cumulative degradation (NCD) method based on the Wiener process. The results show that our proposed burn-in test method performs better than comparable methods.https://www.mdpi.com/1996-1073/11/11/3021cellular phone lithium-ion batteriesrelevance vector machine (RVM)sequential floating forward search (SFFS)classification instability penaltyburn-in test |
| spellingShingle | Jinsong Yu Jie Yang Diyin Tang Jing Dai An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine Energies cellular phone lithium-ion batteries relevance vector machine (RVM) sequential floating forward search (SFFS) classification instability penalty burn-in test |
| title | An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine |
| title_full | An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine |
| title_fullStr | An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine |
| title_full_unstemmed | An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine |
| title_short | An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine |
| title_sort | optimal burn in policy for cellular phone lithium ion batteries using a feature selection strategy and relevance vector machine |
| topic | cellular phone lithium-ion batteries relevance vector machine (RVM) sequential floating forward search (SFFS) classification instability penalty burn-in test |
| url | https://www.mdpi.com/1996-1073/11/11/3021 |
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