Abstract
Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life.
Original language | English |
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Pages (from-to) | 2456-2461 |
Number of pages | 6 |
Journal | Energy Procedia |
Volume | 158 |
DOIs | |
Publication status | Published - Feb 2019 |
Externally published | Yes |
Event | 10th International Conference on Applied Energy, ICAE 2018 - Hong Kong, China Duration: 22 Aug 2018 → 25 Aug 2018 |
Bibliographical note
Publisher Copyright:© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.
Keywords
- Aging trajectory prediction
- Bayesian Monte Carlo
- Lithium-ion batteries
- Model migration
- State-of-health