Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning

Abstract

Real-time and personalized lithium-ion battery health management is conducive to safety improvement for end-users. However, personalized prognostic of battery health status is still challenging due to diverse usage interests, dynamic...

Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

“…A common way to share the data and code related to the work is to provide the access link in the supplementary information of the published paper, 111–114 which makes it easy to find for the readers. To share the data, a public data-sharing platform can be chosen, such as Zenodo, 111,113,114 Mendeley, 115 and OSF, 116 or the personal website is also popular to share the data from the research group. 35,117–119 For code sharing, it is convenient for readers to access when it is shared together with the related datasets, 35,118,119 or a public code sharing platform can be chosen, such as Github.…”

“…These papers showed that the deep learning method has better accuracy and generalization than the feature-based EoL prediction, which would help deep learning-based prediction to become one main trend in this field. Recently, Ma et al 115 generated a dataset of aging batteries under different protocols to be used in their deep learning method for battery EoL prediction with unknown usages. As shown in Fig.…”

“…To share the data, a public data-sharing platform can be chosen, such as Zenodo, 111,113,114 Mendeley, 115 and OSF, 116 or the personal website is also popular to share the data from the research group. 35,[117][118][119] For code sharing, it is convenient for readers to access when it is shared together with the related datasets, 35,118,119 or a public code sharing platform can be chosen, such as Github.…”

“…35,[117][118][119] For code sharing, it is convenient for readers to access when it is shared together with the related datasets, 35,118,119 or a public code sharing platform can be chosen, such as Github. 111,115 The detailed information of each data set is summarized in Table 2, where the battery chemistry and type, the aging cycling profiles, the degradation trends, and the publication year are included. Here the degradation trends mean the overall shape of the degradation curve, which includes sub-linear, linear, and super-linear.…”

“…The convolutional neural network (CNN) and recurrent neural network (RNN) show satisfactory performance in this regard. 115 Therefore, the model is trained directly by the processed raw data and then applied to SoH estimation.…”