Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics
We propose a robust and reliable method based on deep neural networks to estimate the remaining useful life of lithium-ion batteries in electric vehicles. In general, the degradation of a battery can be predicted by monitoring its internal resistance. However, prediction under battery operation cann...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/7/846 |
| _version_ | 1797539090503041024 |
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| author | Chul-Jun Lee Bo-Kyong Kim Mi-Kyeong Kwon Kanghyun Nam Seok-Won Kang |
| author_facet | Chul-Jun Lee Bo-Kyong Kim Mi-Kyeong Kwon Kanghyun Nam Seok-Won Kang |
| author_sort | Chul-Jun Lee |
| collection | DOAJ |
| description | We propose a robust and reliable method based on deep neural networks to estimate the remaining useful life of lithium-ion batteries in electric vehicles. In general, the degradation of a battery can be predicted by monitoring its internal resistance. However, prediction under battery operation cannot be achieved using conventional methods such as electrochemical impedance spectroscopy. The battery state can be predicted based on the change in the capacity according to the state of health. For the proposed method, a statistical analysis of capacity fade considering the impedance increase according to the degree of deterioration is conducted by applying a deep neural network to diverse data from charge/discharge characteristics. Then, probabilistic predictions based on the capacity fade trends are obtained to improve the prediction accuracy of the remaining useful life using another deep neural network. |
| first_indexed | 2024-03-10T12:40:23Z |
| format | Article |
| id | doaj.art-34e488d8b8fe48cfbca2ec7a2995a717 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T12:40:23Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-34e488d8b8fe48cfbca2ec7a2995a7172023-11-21T13:53:51ZengMDPI AGElectronics2079-92922021-04-0110784610.3390/electronics10070846Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge CharacteristicsChul-Jun Lee0Bo-Kyong Kim1Mi-Kyeong Kwon2Kanghyun Nam3Seok-Won Kang4Department of Mechanical Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, KoreaKorea Railroad Research Institute, Uiwang, Gyeonggi 16105, KoreaDepartment of Automotive Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, KoreaDepartment of Mechanical Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, KoreaDepartment of Automotive Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, KoreaWe propose a robust and reliable method based on deep neural networks to estimate the remaining useful life of lithium-ion batteries in electric vehicles. In general, the degradation of a battery can be predicted by monitoring its internal resistance. However, prediction under battery operation cannot be achieved using conventional methods such as electrochemical impedance spectroscopy. The battery state can be predicted based on the change in the capacity according to the state of health. For the proposed method, a statistical analysis of capacity fade considering the impedance increase according to the degree of deterioration is conducted by applying a deep neural network to diverse data from charge/discharge characteristics. Then, probabilistic predictions based on the capacity fade trends are obtained to improve the prediction accuracy of the remaining useful life using another deep neural network.https://www.mdpi.com/2079-9292/10/7/846agingbattery management systemdeep neural network (DNN)particle filter (PF)prognostics and health management (PHM)regression analysis |
| spellingShingle | Chul-Jun Lee Bo-Kyong Kim Mi-Kyeong Kwon Kanghyun Nam Seok-Won Kang Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics Electronics aging battery management system deep neural network (DNN) particle filter (PF) prognostics and health management (PHM) regression analysis |
| title | Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics |
| title_full | Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics |
| title_fullStr | Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics |
| title_full_unstemmed | Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics |
| title_short | Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics |
| title_sort | real time prediction of capacity fade and remaining useful life of lithium ion batteries based on charge discharge characteristics |
| topic | aging battery management system deep neural network (DNN) particle filter (PF) prognostics and health management (PHM) regression analysis |
| url | https://www.mdpi.com/2079-9292/10/7/846 |
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