Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries

• Published in: IEEE transactions on industrial electronics (1982) Vol. 67; no. 10; pp. 8723 - 8731
• Main Authors: Tan, Yandan, Zhao, Guangcai
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Battery charge measurement; Current measurement; Degradation; Feature expression scoring (FES); Learning; Lithium; Lithium-ion batteries; lithium-ion battery (LIB); long short-term memory (LSTM); Noise prediction; Noise reduction; Noise sensitivity; Prediction models; Predictive models; Rechargeable batteries; state-of-health (SOH); Task analysis; Training; transfer learning; Voltage measurement

Existing state-of-health (SOH) data-driven prediction techniques for lithium-ion batteries are subject to mass training data, which leads to limited application. To face the challenge, in this article, we propose a novel SOH prediction method based on transfer learning. The long short-term memory (L...