Issue 10, 2022

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 the battery health status is still challenging due to diverse usage interests, dynamic operational patterns and limited historical data. We generate a comprehensive dataset consisting of 77 commercial cells (77 discharge protocols) with over 140 000 charge–discharge cycles—the largest dataset to our knowledge of its kind, and develop a transfer learning framework to realize real-time personalized health status prediction for unseen battery discharge protocols, at any charge–discharge cycle. Our method can achieve mean testing errors of 0.176% and 8.72% for capacity estimation and remaining useful life (RUL) prediction, respectively. Additionally, the proposed framework can leverage the knowledge from two other well-known battery datasets, with a variety of charge configurations and a different battery chemistry respectively, to reliably estimate the capacity (0.328%/0.193%) and predict the RUL (9.80%/9.90%) of our cells. This study allows end users to tailor battery consumption plans and motivates manufacturers to improve battery designs.

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

Supplementary files

Article information

Article type
Paper
Submitted
24 May 2022
Accepted
28 Jul 2022
First published
30 Jul 2022
This article is Open Access
Creative Commons BY-NC license

Energy Environ. Sci., 2022,15, 4083-4094

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

G. Ma, S. Xu, B. Jiang, C. Cheng, X. Yang, Y. Shen, T. Yang, Y. Huang, H. Ding and Y. Yuan, Energy Environ. Sci., 2022, 15, 4083 DOI: 10.1039/D2EE01676A

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements