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OpenAlex Citation Counts

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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:

Accurate Prediction Approach of SOH for Lithium-Ion Batteries Based on LSTM Method
Lijun Zhang, Tuo Ji, Shihao Yu, et al.
Batteries (2023) Vol. 9, Iss. 3, pp. 177-177
Open Access | Times Cited: 40

Showing 1-25 of 40 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
Reliability enhancement of state of health assessment model of lithium-ion battery considering the uncertainty with quantile distribution of deep features
Ying Zhang, Ming Zhang, Chao Liu, et al.
Reliability Engineering & System Safety (2024) Vol. 245, pp. 110002-110002
Open Access | Times Cited: 15
Fault prognosis of Li-ion batteries in electric vehicles: Recent progress, challenges and prospects
Heng Li, Muaaz Bin Kaleem, Kailong Liu, et al.
Journal of Energy Storage (2025) Vol. 116, pp. 116002-116002
Closed Access | Times Cited: 1
Open access dataset, code library and benchmarking deep learning approaches for state-of-health estimation of lithium-ion batteries
Fujin Wang, Zhi Zhai, Bingchen Liu, et al.
Journal of Energy Storage (2023) Vol. 77, pp. 109884-109884
Closed Access | Times Cited: 23
State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU
Hao Li, Chao Chen, Jie Wei, et al.
Electronics (2024) Vol. 13, Iss. 9, pp. 1675-1675
Open Access | Times Cited: 10
Deep machine learning approaches for battery health monitoring
Sanjeev Kumar Singh, P. R. Budarapu
Energy (2024) Vol. 300, pp. 131540-131540
Closed Access | Times Cited: 10
A hybrid neural network based on variational mode decomposition denoising for predicting state-of-health of lithium-ion batteries
Zifan Yuan, Tian Tian, Fuchong Hao, et al.
Journal of Power Sources (2024) Vol. 609, pp. 234697-234697
Closed Access | Times Cited: 8
A Lithium-Ion Battery Capacity and RUL Prediction Fusion Method Based on Decomposition Strategy and GRU
Huihan Liu, Yanmei Li, Laijin Luo, et al.
Batteries (2023) Vol. 9, Iss. 6, pp. 323-323
Open Access | Times Cited: 19
An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and PSO-GRNN algorithm
Jiaxin Wang, Liqi Zhu, Houde Dai
Journal of Energy Storage (2023) Vol. 72, pp. 108638-108638
Closed Access | Times Cited: 17
An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition
Tao Zhu, Shunli Wang, Yongcun Fan, et al.
Energy (2024) Vol. 306, pp. 132464-132464
Closed Access | Times Cited: 6
Assessing power grid vulnerability to extreme weather: The impact of electric vehicles and energy storage systems
Li Yang, Jiashen Teh, Ching‐Ming Lai
Journal of Energy Storage (2025) Vol. 112, pp. 115534-115534
Closed Access
Transfer learning applying electrochemical degradation indicator combined with long short-term memory network for flexible battery state-of-health estimation
Jaeyeong Kim, Dong‐Ho Han, Pyeong‐Yeon Lee, et al.
eTransportation (2023) Vol. 18, pp. 100293-100293
Closed Access | Times Cited: 13
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
An adaptive semi-supervised self-learning method for online state of health estimation of lithium-ion batteries
Fusheng Jiang, Yi Ren, Ting Tang, et al.
Energy (2024) Vol. 305, pp. 132213-132213
Closed Access | Times Cited: 4
Battery state-of-health estimation: An ultrasonic detection method with explainable AI
Kailong Liu, Jingyang Fang, S. P. Zhao, et al.
Energy (2025) Vol. 319, pp. 134923-134923
Closed 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
Enhanced State of Health Prediction for Lithium-Ion Batteries Using a Hybrid Convolutional–Kolmogorov–Arnold Network
Yunxian Cui, Y.T. Feng
International Journal of Electrochemical Science (2025), pp. 101008-101008
Open Access
Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses
Dengfeng Zhao, Haiyang Li, Fang Zhou, et al.
World Electric Vehicle Journal (2023) Vol. 14, Iss. 6, pp. 145-145
Open Access | Times Cited: 12
Remaining useful life prediction of lithium battery based on ACNN-Mogrifier LSTM-MMD
Zihan Li, Li Ai, Fang Bai, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 1, pp. 016101-016101
Closed Access | Times Cited: 12
A Study on LSTM-Based Lithium Battery SoH Estimation in Urban Railway Vehicle Operating Environments
Hyo-Seok Oh, Jae-Moon Kim, Chin Young Chang
Journal of Electrical Engineering and Technology (2024) Vol. 19, Iss. 4, pp. 2817-2829
Closed Access | Times Cited: 3
A Hyperparameter-Tuned LSTM Technique-Based Battery Remaining Useful Life Estimation Considering Incremental Capacity Curves
K. Dhananjay Rao, A. Ramakrishna, M. Ramesh, et al.
IEEE Access (2024) Vol. 12, pp. 127259-127271
Open Access | Times Cited: 3
Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery
Nan Qi, Kang Yan, Yajuan Yu, et al.
Frontiers in Energy (2023) Vol. 18, Iss. 2, pp. 223-240
Closed Access | Times Cited: 9
State-of-Charge Estimation of Lithium-Ion Battery Based on Convolutional Neural Network Combined with Unscented Kalman Filter
Hongli Ma, Xinyuan Bao, António M. Lopes, et al.
Batteries (2024) Vol. 10, Iss. 6, pp. 198-198
Open Access | Times Cited: 3
State of Health Prediction in Electric Vehicle Batteries Using a Deep Learning Model
Raid Mohsen Alhazmi
World Electric Vehicle Journal (2024) Vol. 15, Iss. 9, pp. 385-385
Open Access | Times Cited: 3
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
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