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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:

Hybrid deep neural network with dimension attention for state-of-health estimation of Lithium-ion Batteries
Xinyuan Bao, Liping Chen, António M. Lopes, et al.
Energy (2023) Vol. 278, pp. 127734-127734
Closed Access | Times Cited: 24

Showing 24 citing articles:

A review of expert hybrid and co-estimation techniques for SOH and RUL estimation in battery management system with electric vehicle application
Turki Alsuwian, Shaheer Ansari, Muhammad Ammirrul Atiqi Mohd Zainuri, et al.
Expert Systems with Applications (2024) Vol. 246, pp. 123123-123123
Closed Access | Times Cited: 34
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
Enhanced Early Prediction of Li-Ion Battery Degradation Using Multicycle Features and an Ensemble Deep Learning Model
Meilia Safitri, Teguh Bharata Adji, Adha Imam Cahyadi
Results in Engineering (2025), pp. 104235-104235
Open Access | Times Cited: 2
State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review
Giovane Ronei Sylvestrin, Joylan Nunes Maciel, Márcio Luís Munhoz Amorim, et al.
Energies (2025) Vol. 18, Iss. 3, pp. 746-746
Open Access | Times Cited: 1
Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework
Kaiyi Yang, Lisheng Zhang, Zhengjie Zhang, et al.
Batteries (2023) Vol. 9, Iss. 7, pp. 351-351
Open Access | Times Cited: 28
An end-cloud collaboration approach for online state-of-health estimation of lithium-ion batteries based on multi-feature and transformer
Wentao Wang, Kaiyi Yang, Lisheng Zhang, et al.
Journal of Power Sources (2024) Vol. 608, pp. 234669-234669
Closed Access | Times Cited: 12
Lithium-ion battery state of health prognostication employing multi-model fusion approach based on image coding of charging voltage and temperature data
Wencan Zhang, Hancheng He, Taotao Li, et al.
Energy (2024) Vol. 296, pp. 131095-131095
Closed Access | Times Cited: 10
An interpretable online prediction method for remaining useful life of lithium-ion batteries
Zuxin Li, Shengyu Shen, Yifu Ye, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
State-of-health and remaining-useful-life estimations of lithium-ion battery based on temporal convolutional network-long short-term memory
Chaoran Li, Xianjie Han, Qiang Zhang, et al.
Journal of Energy Storage (2023) Vol. 74, pp. 109498-109498
Closed Access | Times Cited: 21
A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery
Zhengyi Bao, Jiahao Nie, Huipin Lin, et al.
Energy (2023) Vol. 282, pp. 128306-128306
Closed Access | Times Cited: 16
A novel method for state of health estimation of lithium-ion batteries based on fractional-order differential voltage-capacity curve
Xugang Zhang, Xiyuan Gao, Linchao Duan, et al.
Applied Energy (2024) Vol. 377, pp. 124404-124404
Closed Access | Times Cited: 6
Battery health state prediction based on lightweight neural networks: A review
Longlong Zhang, Shanshuai Wang, Shi Wang, et al.
Ionics (2024)
Closed Access | Times Cited: 5
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: 4
State of charge estimation of lithium-ion batteries with unknown parameters using an adaptive fractional-order center difference Kalman filter
Haoyu Chai, Zhe Gao, Zhiyuan Jiao, et al.
Measurement (2025), pp. 116705-116705
Closed Access
Battery state-of-health estimation based on random charge curve fitting and broad learning system with attention mechanism
Houde Dai, Yiyang Huang, L. Zhu, et al.
Journal of Power Sources (2025) Vol. 636, pp. 236544-236544
Closed Access
A parallel weighted ADTC-Transformer framework with FUnet fusion and KAN for improved lithium-ion battery SOH prediction
Chuang Chen, Yuheng Wu, Jiantao Shi, et al.
Control Engineering Practice (2025) Vol. 159, pp. 106302-106302
Closed Access
Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review
Chenyuan Liu, Heng Li, Kexin Li, et al.
Energies (2025) Vol. 18, Iss. 6, pp. 1463-1463
Open Access
A method for estimating lithium-ion battery state of health based on physics-informed hybrid neural network
Yuxiao Luo, Shenghong Ju, Peichao Li, et al.
Electrochimica Acta (2025), pp. 146110-146110
Closed Access
A hybrid model with Gaussian process-based covariance matrix adaptation evolution strategy for state of charge estimation of lithium-ion batteries
Wei Wang, Yong Wang, Zijun Zhang
Journal of Energy Storage (2025) Vol. 119, pp. 115948-115948
Closed Access
Fractional variable-order observer-based method for state-of-charge estimation of lithium-ion batteries
Xiaobo Wu, Liping Chen, António M. Lopes, et al.
Applied Energy (2025) Vol. 389, pp. 125775-125775
Closed Access
Battery State of Health Estimation from Discharge Voltage Segments Using an Artificial Neural Network
Muhammad Usman Javaid, Jaewon Seo, Young‐Kyoon Suh, et al.
International Journal of Precision Engineering and Manufacturing-Green Technology (2024) Vol. 11, Iss. 3, pp. 863-876
Closed Access | Times Cited: 3
Lithium Battery State of Health Prognostication Employing Multi-Model Fusion Approach Based on Image Coding of Charging Voltage and Temperature Data
Wencan Zhang, Hancheng He, Taotao Li, et al.
(2024)
Closed Access | Times Cited: 1
Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network
Fang Guo, Guangshan Huang, Wencan Zhang, et al.
Energies (2023) Vol. 16, Iss. 24, pp. 8010-8010
Open Access | Times Cited: 4
Enhanced Lithium-Ion Battery SOH Estimation Using Bayesian-Optimized CNN Deep Learning Approach
Huang Xiao-rong, Jionghui Wei, Jieming Huang, et al.
International Journal of Pattern Recognition and Artificial Intelligence (2024) Vol. 38, Iss. 11
Closed Access
Lithium-ion battery SOH estimation method based on multi-feature and CNN-KAN
Zhaohui Zhang, Xin Liu, Runrun Zhang, et al.
Frontiers in Energy Research (2024) Vol. 12
Open Access
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