State of health prediction method for battery cells using bidirectional long short-term memory neural network with time-varying filter empirical mode decomposition
- 1 August 2025
- journal article
- Published by Elsevier in Journal of Power Sources
- Vol. 656
Abstract
No abstract availableFunding Information
- National Science and Technology Council
This publication has 27 references indexed in Scilit:
- Enhancing real-time degradation prediction of lithium-ion battery: A digital twin framework with CNN-LSTM-attention modelEnergy, 2023
- Estimation of internal states in a Li-ion battery using BiLSTM with Bayesian hyperparameter optimizationJournal of Energy Storage, 2023
- Remaining useful life prediction for lithium-ion batteries based on sliding window technique and Box-Cox transformationJournal of Energy Storage, 2023
- Improved LSTM based state of health estimation using random segments of the charging curves for lithium-ion batteriesJournal of Energy Storage, 2023
- A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion batteryEnergy, 2023
- State of health prediction for li-ion batteries with end-to-end deep learningJournal of Energy Storage, 2023
- Estimation of the state of health (SOH) of batteries using discrete curvature feature extractionJournal of Energy Storage, 2022
- Developing an online data-driven approach for prognostics and health management of lithium-ion batteriesApplied Energy, 2022
- Battery monitoring system using machine learningJournal of Energy Storage, 2021
- Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machineEnergy, 2018