About this document
LSTM for Lithium-Ion Battery RUL Prediction by Carlos Barrera is a document available to read on EtoBox.
This article presents a novel method for predicting the remaining useful life (RUL) of lithium-ion batteries using long short-term memory (LSTM) networks, incorporating multi-channel charging profiles of voltage, current, and temperature. The proposed approach significantly improves prediction accuracy, achieving a mean absolute percentage error (MAPE) reduction of up to 63.7% compared to traditional methods. The research highlights the importance of considering capacity regeneration and diverse data for ac
- Author
- Carlos Barrera
- Language
- EN