Early perception of Lithium-ion battery degradation trajectory with graphical features and deep learning DOI

Haichuan Zhao,

Jinhao Meng, Qiao Peng

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 381, P. 125214 - 125214

Published: Dec. 28, 2024

Language: Английский

State of Health Estimation of Lithium-Ion Batteries Based on Feature Optimization and Data-Driven Models DOI

G. G. Mu,

Qingguo Wei,

Yonghong Xu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134578 - 134578

Published: Jan. 1, 2025

Language: Английский

Citations

3
State-of-health estimation and knee point identification of lithium-ion battery based on data-driven and mechanism model DOI
Yulong Ni, Kai Song, Lei Pei

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 385, P. 125539 - 125539

Published: Feb. 17, 2025

Language: Английский

Citations

2
State-of-Health Prediction of Lithium-Ion Batteries Using Feature Fusion and a Hybrid Neural Network Model DOI
Yang Li, Guoqiang Gao, Kui Chen

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135163 - 135163

Published: Feb. 1, 2025

Language: Английский

Citations

2
State of power prediction joint fisher optimal segmentation and PO-BP neural network for a parallel battery pack considering cell inconsistency DOI
Simin Peng, Shengdong Chen, Yong Liu

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 381, P. 125130 - 125130

Published: Dec. 16, 2024

Language: Английский

Citations

6
State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries DOI
Simin Peng, Yujian Wang, Aihua Tang

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 134293 - 134293

Published: Dec. 1, 2024

Language: Английский

Citations

6
A lithium-ion battery state of charge estimation method based on the fusion of data-driven and Kalman filter-based method DOI Creative Commons
Chuanxin Fan, Kailong Liu,

Chunfei Gu

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100048 - 100048

Published: March 1, 2025

Language: Английский

Citations

0
Remaining useful life prediction for solid-state lithium batteries based on spatial-temporal relations and neuronal ODE-assisted KAN DOI
Zhenxi Wang,

Yan Ma,

Jinwu Gao

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 111003 - 111003

Published: March 1, 2025

Language: Английский

Citations

0
State of health estimation for lithium-ion batteries based on optimal feature subset algorithm DOI
Jing Sun, Haitao Wang

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135685 - 135685

Published: March 1, 2025

Language: Английский

Citations

0
Online Remaining Useful Life Prediction of Lithium-ion Batteries Based on Hybrid Model DOI
Jing Sun,

Huiyi Yan

Journal of The Electrochemical Society, Journal Year: 2025, Volume and Issue: 172(4), P. 040503 - 040503

Published: April 1, 2025

A hybrid model based on black-winged kite algorithm and dual-attention mechanism optimized temporal convolutional network (TCN) with simple recurrent unit (SRU) is proposed to improve the accuracy of online remaining-useful-life (RUL) prediction for Li-ion batteries (LIBs). Health indicators (HIs) correlated battery capacity are extracted from calculated variables verified Spearman correlation coefficient constructed, applying TCN multi-head self-attention capture in spatial dimension decay pattern HIs, introducing attention ability SRU timing patterns input sequences as well BKA further optimize hyper-parameters, enhancing performance. Experimental data used validate model’s predictive performance LIBs at different usage levels under complex conditions such regeneration, sharp fluctuations, plunges. The results achieve MAE less than 3.66%, MAPE below 2.02%, RMSE not exceeding 5.03%, R 2 greater 0.96, absolute error RUL 5. experimental demonstrate that can accurate perform good robustness.

Language: Английский

Citations

0
Physics-based battery SOC estimation: A joint Data-driven and window-varying adaptive extended Kalman filter approach DOI

Chunfei Gu,

Lianghua Ni,

Xinxiang Tian

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 121, P. 116465 - 116465

Published: April 11, 2025

Language: Английский

Citations

0