Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model

Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model

Achieving high accuracy in the early prediction of Li-ion battery degradation is challenging owing to the nonlinear and dynamic nature of battery aging. This study introduces a GRU-LSTM ensemble model that combines Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks to forecast th...

Full description

Saved in:
Bibliographic Details
Main Authors: Meilia Safitri, Teguh Bharata Adji, Adha Imam Cahyadi
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Results in Engineering
Subjects:
GRU-LSTM ensemble
Lithium-ion battery degradation
Early prediction
Feature-based machine learning
Battery management system (BMS)
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025003214
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items