Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery
On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite L...
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| Format: | Article |
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MDPI AG
2018-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/8/11/2078 |
| _version_ | 1811332711005028352 |
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| author | Cunsong Wang Ningyun Lu Senlin Wang Yuehua Cheng Bin Jiang |
| author_facet | Cunsong Wang Ningyun Lu Senlin Wang Yuehua Cheng Bin Jiang |
| author_sort | Cunsong Wang |
| collection | DOAJ |
| description | On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods. |
| first_indexed | 2024-04-13T16:41:00Z |
| format | Article |
| id | doaj.art-9fc7f4f039f04053a4b9314557954940 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-13T16:41:00Z |
| publishDate | 2018-10-01 |
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| series | Applied Sciences |
| spelling | doaj.art-9fc7f4f039f04053a4b93145579549402022-12-22T02:39:14ZengMDPI AGApplied Sciences2076-34172018-10-01811207810.3390/app8112078app8112078Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion BatteryCunsong Wang0Ningyun Lu1Senlin Wang2Yuehua Cheng3Bin Jiang4College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaQuanzhou Institute of Equipment Manufacturing Haixi Institutes, Chinese Academy of Science, Quanzhou 362200, ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaOn-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods.https://www.mdpi.com/2076-3417/8/11/2078Lithium-ion batteryremaining useful lifehealth indictorlong short-term memory |
| spellingShingle | Cunsong Wang Ningyun Lu Senlin Wang Yuehua Cheng Bin Jiang Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery Applied Sciences Lithium-ion battery remaining useful life health indictor long short-term memory |
| title | Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery |
| title_full | Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery |
| title_fullStr | Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery |
| title_full_unstemmed | Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery |
| title_short | Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery |
| title_sort | dynamic long short term memory neural network based indirect remaining useful life prognosis for satellite lithium ion battery |
| topic | Lithium-ion battery remaining useful life health indictor long short-term memory |
| url | https://www.mdpi.com/2076-3417/8/11/2078 |
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