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

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

Innovative multiscale fusion - antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries DOI

Junjie Tao,

Shunli Wang, Wen Cao

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133541 - 133541

Published: Oct. 1, 2024

Language: Английский

Citations

Joint estimation of SOC and peak power capability for series reused battery pack based on screening process method DOI

Yujie Zhang, Baicheng Liu, Hongguang Zhang

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133940 - 133940

Published: Nov. 1, 2024

Language: Английский

Citations

AM-MFF: A multi-feature fusion framework based on attention mechanism for robust and interpretable lithium-ion battery state of health estimation DOI

Sizhe Chen,

Jing Liu,

Haoliang Yuan

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 381, P. 125116 - 125116

Published: Dec. 14, 2024

Language: Английский

Citations

An innovative multitask learning - long short-term memory neural network for the online anti-aging state of charge estimation of lithium-ion batteries adaptive to varying temperature and current conditions DOI

Junjie Tao,

Shunli Wang, Wen Cao

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 134272 - 134272

Published: Dec. 1, 2024

Language: Английский

Citations

State of Health Estimation for Lithium-Ion Batteries Based on Fusion Health Features and Adaboost-GWO-BP Model DOI

Tong Liang,

Yiyang Li,

Bin He

et al.

Journal of The Electrochemical Society, Journal Year: 2024, Volume and Issue: 171(11), P. 110528 - 110528

Published: Nov. 1, 2024

To accurately predict the state of health (SOH) lithium-ion batteries and improve safety reliability battery management systems, a new SOH estimation method based on fusion features (HFs) adaptive boosting integrated grey wolf optimizer to optimize back propagation neural network (Adaboost-GWO-BP) is proposed. First, five kinds multi-type HFs were extracted from charging process, correlation between proposed was verified by Pearson Spearman coefficients. Then, indirect feature (IHF) obtained multidimensional scaling dimensionality reduction reduce data redundancy SOH. The GWO-BP model then used establish nonlinear mapping relationship IHF In order overcome problem low accuracy in single model, Adaboost algorithm ensemble learning introduced enhance estimation. Finally, NASA dataset, compared with other models. comparative experiments, mean absolute error root square for less than 0.81% 1.26%, which has higher

Language: Английский

Citations

State of health estimation method for Lithium-ion batteries based on multi-feature fusion and BO-BiGRU model DOI

Junchao Zhu, Jun Zhang, Jian Kang

et al.

Journal of Electrochemical Energy Conversion and Storage, Journal Year: 2024, Volume and Issue: 22(4)

Published: Oct. 14, 2024

Abstract The state of health (SOH) lithium-ion batteries is a crucial parameter for assessing battery degradation. aim this study to solve the problems single extraction features (HFs) and redundancy information between in SOH estimation. This article develops an estimation method based on multifeature fusion Bayesian optimization (BO)-bidirectional gated recurrent unit (BiGRU) model. First, total eight HFs three categories, namely, time, energy, probability, can be extracted from charging data accurately describe aging mechanism battery. Pearson Spearman analysis verified strong correlation SOH. Second, multiple principal components obtained by kernel component (KPCA) eliminate HFs. with highest selected bicorrelation defined as fused HF. Finally, improve accuracy, BO-BiGRU model proposed. proposed validated using datasets NASA. results show that accuracy high, while mean absolute error (MAE) lower than 1.2%. In addition, lithium estimated different proportions test sets, root-mean-square (RMSE) percentage (MAPE) remain within 3%, high robustness.

Language: Английский

Citations

BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator DOI

Xiaopeng Li, Minghang Zhao, Shisheng Zhong

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 134030 - 134030

Published: Nov. 1, 2024

Language: Английский

Citations

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

Citations