期刊
ENERGY & ENVIRONMENTAL SCIENCE
卷 15, 期 10, 页码 4083-4094出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d2ee01676a
关键词
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资金
- National Key R&D Program of China [2018YFB1701202]
- National Natural Science Foundation of China [92167201]
This study presents a real-time personalized lithium-ion battery health management method, which can predict the health status for unseen battery discharge protocols in real-time using a comprehensive dataset and a transfer learning framework. The method allows end-users to tailor battery consumption plans and motivates manufacturers to improve battery designs.
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.
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