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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:

State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks
Yvxin He, Zhongwei Deng, Jue Chen, et al.
Journal of Energy Chemistry (2024) Vol. 98, pp. 1-11
Closed Access | Times Cited: 12

Showing 12 citing articles:

State of charge prediction for lithium-ion batteries based on multi-process scale encoding and adaptive graph convolution
Hongyan Wang, Wei Wu, Langfu Cui, et al.
Journal of Energy Storage (2025) Vol. 113, pp. 115482-115482
Closed Access | Times Cited: 1
Enhanced battery life prediction with reduced data demand via semi-supervised representation learning
Liang Ma, Jinpeng Tian, Tieling Zhang, et al.
Journal of Energy Chemistry (2024)
Open Access | Times Cited: 5
A Real‐Time Adaptive Machine Learning Charging and Neural Network Balancing Mechanism of Lithium‐Ion Battery Pack
Gaurav Malik, Manish Kumar Saini
Energy Storage (2025) Vol. 7, Iss. 1
Closed Access
Data-Optimization Based SOC-SOH Estimation for Lithium-Ion Batteries with Current Bias Compensation
Min Ye, Gaoqi Lian, Wei Li, et al.
Energy (2025), pp. 135490-135490
Closed Access
Stochastic state of health estimation for lithium-ion batteries with automated feature fusion using quantum convolutional neural network
Liang Chen, Shengyu Tao, Xinghao Huang, et al.
Journal of Energy Chemistry (2025)
Closed Access
Graph-guided fault detection for multi-type lithium-ion batteries in realistic electric vehicles optimized by ensemble learning
Caiping Zhang, Shuowei Li, Jingcai Du, et al.
Journal of Energy Chemistry (2025)
Closed Access
AI enabled fast charging of lithium-ion batteries of electric vehicles during their life cycle: review, challenges and perspectives
Daoming Sun, Dongxu Guo, Yufang Lu, et al.
Energy & Environmental Science (2024)
Closed Access | Times Cited: 2
Unlocking the potential of unlabeled data: Self-supervised machine learning for battery aging diagnosis with real-world field data
Qiao Wang, Min Ye, Sehriban Celik, et al.
Journal of Energy Chemistry (2024) Vol. 99, pp. 681-691
Open Access
A Method for Estimating the SOH of Lithium-Ion Batteries Based on Graph Perceptual Neural Network
K.M. Chen, Dandan Wang, Wenwen Guo
Batteries (2024) Vol. 10, Iss. 9, pp. 326-326
Open 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
Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development
Yang Xiao, Xiong Shi, Xiangmin Li, et al.
Energy Storage (2024) Vol. 6, Iss. 8
Closed Access
A novel state of health estimation model for lithium-ion batteries incorporating signal processing and optimized machine learning methods
Xing Zhang, Juqiang Feng, Feng Cai, et al.
Frontiers in Energy (2024)
Closed Access
DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial-temporal fusion attention
Zheng Chen, Quan Qian
Energy and AI (2024), pp. 100462-100462
Open Access
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