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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Estimation of SoH and internal resistances of Lithium ion battery based on LSTM network
Chí Nguyễn Văn, Duy Ta Quang
International Journal of Electrochemical Science (2023) Vol. 18, Iss. 6, pp. 100166-100166
Open Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model
Jiani Zhou, Shunli Wang, Wen Cao, et al.
Electrochimica Acta (2024) Vol. 487, pp. 144146-144146
Closed Access | Times Cited: 16
Machine Learning-Based Lithium Battery State of Health Prediction Research
Kun Li, Xinling Chen
Applied Sciences (2025) Vol. 15, Iss. 2, pp. 516-516
Open Access | Times Cited: 1
A novel prediction model of grounding resistance based on long short-term memory
Xuewen Pu, Jing Zhang, Fei Wang, et al.
AIP Advances (2025) Vol. 15, Iss. 1
Open Access
Fine-Tuned Transfer Learning and Deep Gated Recurrent Unit Methods for State-of-Health Estimation of the Whole Life-Cycle of Lithium-Ion Batteries
Zhenglin Guo, Jian Wang, Qiang Fu, et al.
International Journal of Electrochemical Science (2025), pp. 100931-100931
Open Access
Diagnosing Rapidly Degrading Lithium Ion Battery Cells Using Direct Current Internal Resistance
J.-J. Lee, Seonyoung Jegal, Mikyung Chung, et al.
(2025)
Closed Access
A decade of machine learning in lithium-ion battery state estimation: a systematic review
Zaina Al-Hashimi, Taha Khamis, Mouaz Al Kouzbary, et al.
Ionics (2025) Vol. 31, Iss. 3, pp. 2351-2377
Closed Access
State of Health Estimation of Lithium-Ion Batteries Based on Hybrid Neural Networks with Residual Connections
Xugang Zhang, Ze Wang, Qingshan Gong, et al.
Journal of The Electrochemical Society (2025) Vol. 172, Iss. 2, pp. 020503-020503
Open Access
Lithium-Ion Batteries state of health estimation based on optimized TCN-GRU-WNN
Nan Zhang, Jing Li, Yunfeng Ma, et al.
Energy Reports (2025) Vol. 13, pp. 2502-2515
Closed Access
Random Forest-Based Grouping for Accurate SOH Estimation in Second-Life Batteries
Joelton Deonei Gotz, José Rodolfo Galvão, Fernanda Cristina Corrêa, et al.
Vehicles (2024) Vol. 6, Iss. 2, pp. 799-813
Open Access | Times Cited: 4
State of Health Estimation for Lithium-Ion Batteries with Deep Learning Approach and Direct Current Internal Resistance
Zhongxian Sun, Weilin He, Junlei Wang, et al.
Energies (2024) Vol. 17, Iss. 11, pp. 2487-2487
Open Access | Times Cited: 4
Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model
Zhao Zhang, Runrun Zhang, Xin Liu, et al.
Batteries (2024) Vol. 10, Iss. 12, pp. 433-433
Open Access | Times Cited: 3
Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries
Slimane Arbaoui, Ahmed Samet, Ali Ayadi, et al.
Energy and AI (2024) Vol. 17, pp. 100413-100413
Open Access | Times Cited: 2
A Temporal Fusion Memory Network-Based Method for State-of-Health Estimation of Lithium-Ion Batteries
K.M. Chen, Dandan Wang, Wenwen Guo
Batteries (2024) Vol. 10, Iss. 8, pp. 286-286
Open Access | Times Cited: 2
State of health estimation method based on real data of electric vehicles using federated learning
Xiaoxin Lv, Yi Cheng, Shidian Ma, et al.
International Journal of Electrochemical Science (2024) Vol. 19, Iss. 8, pp. 100591-100591
Open Access | Times Cited: 2
State of health estimation for lithium-ion batteries based on improved bat algorithm optimization kernel extreme learning machine
Xiangbin Li, Diqing Fan, Xintian Liu, et al.
Journal of Energy Storage (2024) Vol. 101, pp. 113756-113756
Closed Access | Times Cited: 2
Lithium-Ion Battery State-of-Health Prediction for New-Energy Electric Vehicles Based on Random Forest Improved Model
Zijun Liang, Ruihan Wang, Xuejuan Zhan, et al.
Applied Sciences (2023) Vol. 13, Iss. 20, pp. 11407-11407
Open Access | Times Cited: 5
Innovative multiscale fusion - antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries
Junjie Tao, Shunli Wang, Wen Cao, et al.
Energy (2024), pp. 133541-133541
Closed Access | Times Cited: 1
Application of state of health estimation and remaining useful life prediction for lithium-ion batteries based on AT-CNN-BiLSTM
Fengming Zhao, D. Gao, Yuan-Ming Cheng, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-bi-lstm-attention
Jarin Tasnim, Md. Azizur Rahman, Md. Shoaib Akhter Rafi, et al.
e-Prime - Advances in Electrical Engineering Electronics and Energy (2024) Vol. 11, pp. 100870-100870
Open Access | Times Cited: 1
State of Health Estimation of Lithium-Ion Battery Using Multi-Health Features Based on Savitzky–Golay Filter and Fitness-Distance Balance- and Lévy Roulette-Enhanced Coyote Optimization Algorithm-Optimized Long Short-Term Memory
Hongbiao Li, Dan Gao, Ling Shi, et al.
Processes (2024) Vol. 12, Iss. 10, pp. 2284-2284
Open Access | Times Cited: 1
Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA
Yujie Zhou, Chaolong Zhang, Xulong Zhang, et al.
World Electric Vehicle Journal (2024) Vol. 15, Iss. 7, pp. 280-280
Open Access
Aging trajectory prediction of lithium-ion batteries based on mechanical-electrical features via nonlinear autoregressive and regression neural networks
Lili Gong, Junjie Ding, Kai Sun, et al.
Journal of Energy Storage (2024) Vol. 105, pp. 114696-114696
Closed Access
State of health estimation based on PSO-SA-LSTM for fast-charge lithium-ion batteries
Liangliang Wei, Qi Diao, Yiwen Sun, et al.
Ionics (2024)
Closed Access
The Joint Estimation of SOC-SOH for Lithium-Ion Batteries Based on BiLSTM-SA
Lingling Wu, Chao Chen, Zhenhua Li, et al.
Electronics (2024) Vol. 14, Iss. 1, pp. 97-97
Open Access
Optimal Active State-of-Charge Balancing with Temperature Control for Lithium-Ion Battery Cells Considering the Aging Effect
Chí Nguyễn Văn, Duy Ta Quang
Research Square (Research Square) (2024)
Open Access
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