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Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics
| Content Provider | MDPI |
|---|---|
| Author | Lee, Chul-Jun Kim, Bo-Kyong Kwon, Mi-Kyeong Nam, Kanghyun Kang, Seok-Won |
| Copyright Year | 2021 |
| 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. |
| Starting Page | 846 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics10070846 |
| Journal | Electronics |
| Issue Number | 7 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-04-01 |
| Access Restriction | Open |
| Subject Keyword | Electronics Industrial Engineering Aging Battery Management System Deep Neural Network (dnn) Particle Filter (pf) Prognostics and Health Management (phm) Regression Analysis Remaining Useful Life (rul) |
| Content Type | Text |
| Resource Type | Article |
