xmlui.dri2xhtml.METS-1.0.item-share
Save the reference
Title
Calendar Ageing Model for Li-Ion Batteries Using Transfer Learning Methods
xmlui.dri2xhtml.METS-1.0.item-contributorOtherinstitution
https://ror.org/03hp1m080Version
http://purl.org/coar/version/c_970fb48d4fbd8a85
Rights
© 2021 by the authors. Licensee MDPIAccess
http://purl.org/coar/access_right/c_abf2Publisher’s version
https://doi.org/10.3390/wevj12030145Published at
World Electric Vehicle Journal Vol 12. N. 3. N. artículo. 145, 2021Publisher
MDPIKeywords
machine learning
transfer learning
lithium-ion batteries
calendar ageing ... [+]
transfer learning
lithium-ion batteries
calendar ageing ... [+]
machine learning
transfer learning
lithium-ion batteries
calendar ageing
artificial neural network [-]
transfer learning
lithium-ion batteries
calendar ageing
artificial neural network [-]
Abstract
Getting accurate lifetime predictions for a particular cell chemistry remains a challenging process, largely dependent on time and cost-intensive experimental battery testing. This paper proposes a tr ... [+]
Getting accurate lifetime predictions for a particular cell chemistry remains a challenging process, largely dependent on time and cost-intensive experimental battery testing. This paper proposes a transfer learning (TL) method to develop LIB ageing models, which allow for the leveraging of experimental laboratory testing data previously obtained for a different cell technology. The TL method is implemented through Neural Networks models, using LiNiMnCoO2/C laboratory ageing data as a baseline model. The obtained TL model achieves an 1.01% overall error for a broad range of operating conditions, using for retraining only two experimental ageing tests of LiFePO4/C cells. [-]
Collections
- Articles - Engineering [683]
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as Attribution 4.0 International
