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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:

Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries
Ding Pan, Xiaojuan Liu, Huiqin Li, et al.
Renewable and Sustainable Energy Reviews (2021) Vol. 148, pp. 111287-111287
Closed Access | Times Cited: 101

Showing 1-25 of 101 citing articles:

Prognostics and health management of Lithium-ion battery using deep learning methods: A review
Ying Zhang, Yan‐Fu Li
Renewable and Sustainable Energy Reviews (2022) Vol. 161, pp. 112282-112282
Closed Access | Times Cited: 156
Remaining useful life prediction of lithium-ion batteries using a hybrid model
Fang Yao, Wenxuan He, Youxi Wu, et al.
Energy (2022) Vol. 248, pp. 123622-123622
Closed Access | Times Cited: 88
The development of machine learning-based remaining useful life prediction for lithium-ion batteries
Xingjun Li, Dan Yu, Søren Byg Vilsen, et al.
Journal of Energy Chemistry (2023) Vol. 82, pp. 103-121
Open Access | Times Cited: 85
Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries
Zhuqing Wang, Ning Liu, Chilian Chen, et al.
Information Sciences (2023) Vol. 635, pp. 398-413
Closed Access | Times Cited: 74
An overview of data-driven battery health estimation technology for battery management system
Minzhi Chen, Guijun Ma, Weibo Liu, et al.
Neurocomputing (2023) Vol. 532, pp. 152-169
Closed Access | Times Cited: 73
Applications of artificial neural network based battery management systems: A literature review
Mehmet Kurucan, Mete Özbaltan, Zekí Yetgín, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 192, pp. 114262-114262
Closed Access | Times Cited: 65
A multilevel optimization approach for daily scheduling of combined heat and power units with integrated electrical and thermal storage
Jiang Hu, Yunhe Zou, N. S. Soltanov
Expert Systems with Applications (2024) Vol. 250, pp. 123729-123729
Closed Access | Times Cited: 62
Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network
Fei Guo, Xiongwei Wu, Lili Liu, et al.
Energy (2023) Vol. 270, pp. 126880-126880
Closed Access | Times Cited: 56
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: 43
AttMoE: Attention with Mixture of Experts for remaining useful life prediction of lithium-ion batteries
Daoquan Chen, Xiuze Zhou
Journal of Energy Storage (2024) Vol. 84, pp. 110780-110780
Closed Access | Times Cited: 17
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
A Bayesian Deep Learning Framework for RUL Prediction Incorporating Uncertainty Quantification and Calibration
Yan‐Hui Lin, Gang-Hui Li
IEEE Transactions on Industrial Informatics (2022) Vol. 18, Iss. 10, pp. 7274-7284
Closed Access | Times Cited: 60
A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena
Huixing Meng, Mengyao Geng, Jinduo Xing, et al.
Energy (2022) Vol. 261, pp. 125278-125278
Closed Access | Times Cited: 57
Remaining useful life prediction of lithium-ion batteries based on attention mechanism and bidirectional long short-term memory network
Zhen Zhang, Wentao Zhang, Kuo Yang, et al.
Measurement (2022) Vol. 204, pp. 112093-112093
Closed Access | Times Cited: 48
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
A Lithium-Ion Battery Degradation Prediction Model With Uncertainty Quantification for Its Predictive Maintenance
Chuang Chen, Guanye Tao, Jiantao Shi, et al.
IEEE Transactions on Industrial Electronics (2023) Vol. 71, Iss. 4, pp. 3650-3659
Closed Access | Times Cited: 28
Towards High-Safety Lithium-Ion Battery Diagnosis Methods
Yulong Zhang, Meng Jiang, Yuhong Zhou, et al.
Batteries (2023) Vol. 9, Iss. 1, pp. 63-63
Open Access | Times Cited: 25
Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries
Yang Li, Shunli Wang, Lei Chen, et al.
Energy (2023) Vol. 282, pp. 128776-128776
Open Access | Times Cited: 23
A novel hybrid scheme for remaining useful life prognostic based on secondary decomposition, BiGRU and error correction
Ting Zhu, Wenbo Wang, Min Yu
Energy (2023) Vol. 276, pp. 127565-127565
Closed Access | Times Cited: 21
Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm
Yifei Zhou, Shunli Wang, Yanxing Xie, et al.
Energy (2024) Vol. 300, pp. 131575-131575
Closed Access | Times Cited: 14
Optimizing solar-wind hybrid energy systems for sustainable charging stations and commercial applications: A two-stage framework with ebola-inspired optimization
Guiyan Zhu, Guogang Yan, Davoud Garmroudi
Expert Systems with Applications (2024) Vol. 246, pp. 123180-123180
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: 9
Remaining Useful Life Prediction of Lithium-Ion Battery via a Sequence Decomposition and Deep Learning Integrated Approach
Zhang Chen, Liqun Chen, Wenjing Shen, et al.
IEEE Transactions on Vehicular Technology (2021) Vol. 71, Iss. 2, pp. 1466-1479
Closed Access | Times Cited: 53
State of health prediction for li-ion batteries with end-to-end deep learning
Chunxiang Zhu, Mingyu Gao, Zhiwei He, et al.
Journal of Energy Storage (2023) Vol. 65, pp. 107218-107218
Closed Access | Times Cited: 21
Convolutional Transformer-Based Multiview Information Perception Framework for Lithium-Ion Battery State-of-Health Estimation
Tianyou Bai, Huan Wang
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-12
Closed Access | Times Cited: 21
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