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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:

Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning
Seongyoon Kim, Yun Young Choi, Ki Jae Kim, et al.
Journal of Energy Storage (2021) Vol. 41, pp. 102893-102893
Closed Access | Times Cited: 96

Showing 1-25 of 96 citing articles:

An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty
Jiusi Zhang, Yuchen Jiang, Xiang Li, et al.
Reliability Engineering & System Safety (2022) Vol. 222, pp. 108357-108357
Closed Access | Times Cited: 114
A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries
Zhong Ren, Changqing Du
Energy Reports (2023) Vol. 9, pp. 2993-3021
Open Access | Times Cited: 112
Remaining Useful Life Prediction of Lithium-Ion Battery With Adaptive Noise Estimation and Capacity Regeneration Detection
Jiusi Zhang, Yuchen Jiang, Xiang Li, et al.
IEEE/ASME Transactions on Mechatronics (2022) Vol. 28, Iss. 2, pp. 632-643
Closed Access | Times Cited: 107
Two-stage aging trajectory prediction of LFP lithium-ion battery based on transfer learning with the cycle life prediction
Ziyou Zhou, Yonggang Liu, Mingxing You, et al.
Green Energy and Intelligent Transportation (2022) Vol. 1, Iss. 1, pp. 100008-100008
Open Access | Times Cited: 101
Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism
Fu‐Kwun Wang, Zemenu Endalamaw Amogne, Jia‐Hong Chou, et al.
Energy (2022) Vol. 254, pp. 124344-124344
Closed Access | Times Cited: 100
Deep and transfer learning for building occupancy detection: A review and comparative analysis
Aya Nabil Sayed, Yassine Himeur, Fayçal Bensaali
Engineering Applications of Artificial Intelligence (2022) Vol. 115, pp. 105254-105254
Open Access | Times Cited: 88
Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects
Kailong Liu, Qiao Peng, Yunhong Che, et al.
Advances in Applied Energy (2022) Vol. 9, pp. 100117-100117
Open Access | Times Cited: 87
Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Yassine Himeur, Mariam Elnour, Fodil Fadli, et al.
Sustainable Cities and Society (2022) Vol. 85, pp. 104059-104059
Open Access | Times Cited: 86
A hybrid neural network model with attention mechanism for state of health estimation of lithium-ion batteries
Aihua Tang, Yihan Jiang, Quanqing Yu, et al.
Journal of Energy Storage (2023) Vol. 68, pp. 107734-107734
Closed Access | Times Cited: 78
State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network
Simin Peng, Yunxiang Sun, Dandan Liu, et al.
Energy (2023) Vol. 282, pp. 128956-128956
Closed Access | Times Cited: 74
Lithium-ion battery health estimation with real-world data for electric vehicles
Jiaqiang Tian, Xinghua Liu, Siqi Li, et al.
Energy (2023) Vol. 270, pp. 126855-126855
Closed Access | Times Cited: 72
State-of-health estimation of lithium-ion batteries based on improved long short-term memory algorithm
Gong Ya-dong, Xiaoyong Zhang, Dianzhu Gao, et al.
Journal of Energy Storage (2022) Vol. 53, pp. 105046-105046
Closed Access | Times Cited: 69
SOH prediction for Lithium-Ion batteries by using historical state and future load information with an AM-seq2seq model
Cheng Qian, Binghui Xu, Quan Xia, et al.
Applied Energy (2023) Vol. 336, pp. 120793-120793
Open Access | Times Cited: 55
Research progress and application of deep learning in remaining useful life, state of health and battery thermal management of lithium batteries
Wenbin He, Zongze Li, Ting Liu, et al.
Journal of Energy Storage (2023) Vol. 70, pp. 107868-107868
Closed Access | Times Cited: 42
A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data
Zicheng Fei, Zijun Zhang, Fangfang Yang, et al.
Journal of Energy Storage (2023) Vol. 62, pp. 106903-106903
Closed Access | Times Cited: 40
Machine learning for battery systems applications: Progress, challenges, and opportunities
Zahra Nozarijouybari, Hosam K. Fathy
Journal of Power Sources (2024) Vol. 601, pp. 234272-234272
Closed Access | Times Cited: 27
Insights and reviews on battery lifetime prediction from research to practice
Xudong Qu, Dapai Shi, Jingyuan Zhao, et al.
Journal of Energy Chemistry (2024) Vol. 94, pp. 716-739
Closed Access | Times Cited: 24
State of health estimation of lithium-ion battery using dual adaptive unscented Kalman filter and Coulomb counting approach
H.M.A. Fahmy, Hany M. Hasanien, Ibrahim Alsaleh, et al.
Journal of Energy Storage (2024) Vol. 88, pp. 111557-111557
Closed Access | Times Cited: 15
Progress in estimating the state of health using transfer learning–based electrochemical impedance spectroscopy of lithium-ion batteries
Guangheng Qi, Guangwen Du, Kai Wang
Ionics (2025) Vol. 31, Iss. 3, pp. 2337-2349
Closed Access | Times Cited: 1
State of Charge and State of Health Estimation in Electric Vehicles: Challenges, Approaches and Future Directions
Babatunde D. Soyoye, Indranil Bhattacharya, Mary Vinolisha Anthony Dhason, et al.
Batteries (2025) Vol. 11, Iss. 1, pp. 32-32
Open Access | Times Cited: 1
A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions
Friedrich von Bülow, Tobias Meisen
Journal of Energy Storage (2022) Vol. 57, pp. 105978-105978
Closed Access | Times Cited: 49
Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization
Liqianyun Xu, Feng Wu, Renjie Chen, et al.
Energy storage materials (2023) Vol. 59, pp. 102785-102785
Closed Access | Times Cited: 39
Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives
Chuan Li, Huahua Zhang, Ping Ding, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 184, pp. 113576-113576
Closed Access | Times Cited: 39
State of health estimation for lithium-ion batteries on few-shot learning
Shuxin Zhang, Zhitao Liu, Hongye Su
Energy (2023) Vol. 268, pp. 126726-126726
Closed Access | Times Cited: 31
Data-Driven Methods for Predicting the State of Health, State of Charge, and Remaining Useful Life of Li-Ion Batteries: A Comprehensive Review
Eunsong Kim, Minseon Kim, Juo Kim, et al.
International Journal of Precision Engineering and Manufacturing (2023) Vol. 24, Iss. 7, pp. 1281-1304
Closed Access | Times Cited: 31
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