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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:

A physics-informed dynamic deep autoencoder for accurate state-of-health prediction of lithium-ion battery
Zhaoyi Xu, Yanjie Guo, Joseph H. Saleh
Neural Computing and Applications (2022) Vol. 34, Iss. 18, pp. 15997-16017
Closed Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities
Huiqin Li, Zhengxin Zhang, Tianmei Li, et al.
Mechanical Systems and Signal Processing (2024) Vol. 209, pp. 111120-111120
Closed Access | Times Cited: 65
Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR
Chunling Wu, Juncheng Fu, Xinrong Huang, et al.
Energies (2023) Vol. 16, Iss. 10, pp. 3993-3993
Open Access | Times Cited: 37
IoB: Internet-of-batteries for electric Vehicles–Architectures, opportunities, and challenges
Heng Li, Muaaz Bin Kaleem, Zhijun Liu, et al.
Green Energy and Intelligent Transportation (2023) Vol. 2, Iss. 6, pp. 100128-100128
Open Access | Times Cited: 34
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: 27
A method for estimating the state of health of lithium-ion batteries based on physics-informed neural network
Jinhua Ye, Xie Quan, Mingqiang Lin, et al.
Energy (2024) Vol. 294, pp. 130828-130828
Closed Access | Times Cited: 10
Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries
RAFAEL SAADI DANTAS TEIXEIRA, Rodrigo Flora Calili, Maria Fatima Ludovico de Almeida, et al.
Batteries (2024) Vol. 10, Iss. 3, pp. 111-111
Open Access | Times Cited: 10
A survey of deep learning-driven architecture for predictive maintenance
Zhe Li, Qian He, Jingyue Li
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108285-108285
Open Access | Times Cited: 8
Artificial intelligence-based data-driven prognostics in industry: A survey
Mohamed A. El-Brawany, Dina A. Ibrahim, Hamdy K. Elminir, et al.
Computers & Industrial Engineering (2023) Vol. 184, pp. 109605-109605
Closed Access | Times Cited: 21
Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries
Tobias Hofmann, Jacob Hamar, Marcel Rogge, et al.
Journal of The Electrochemical Society (2023) Vol. 170, Iss. 9, pp. 090524-090524
Open Access | Times Cited: 16
Hybrid physics-infused 1D-CNN based deep learning framework for diesel engine fault diagnostics
Shubhendu Kumar Singh, Raj Pradip Khawale, Subhashis Hazarika, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 28, pp. 17511-17539
Closed Access | Times Cited: 5
Physics-informed deep learning framework for explainable remaining useful life prediction
Minjae Kim, Sihyun Yoo, Seho Son, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 143, pp. 110072-110072
Closed Access
An Adaptive and Interpretable SOH Estimation Method for Lithium-ion Batteries based-on Relaxation Voltage Cross-scale Features and Multi-LSTM-RFR2
Guangzheng Lyu, Heng Zhang, Qiang Miao
Energy (2024) Vol. 304, pp. 132167-132167
Closed Access | Times Cited: 4
Integration of lithium-ion battery recycling into manufacturing through digitalization: A perspective
Imelda Cardenas-Sierra, Utkarsh Vijay, Frédéric Aguesse, et al.
Journal of Power Sources (2025) Vol. 631, pp. 236158-236158
Open Access
A novel ensemble learning model for state of health estimation of lithium-ion batteries
Chunhua Zeng, Cheng Xu, Haomiao Li, et al.
Journal of Power Sources (2025) Vol. 638, pp. 236608-236608
Closed Access
Thermal conductivity estimation using Physics-Informed Neural Networks with limited data
Junhyoung Jo, Yeonhwi Jeong, Jinsu Kim, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 137, pp. 109079-109079
Closed Access | Times Cited: 3
An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features
Guangzheng Lyu, Heng Zhang, Qiang Miao
Energy (2023) Vol. 283, pp. 129067-129067
Closed Access | Times Cited: 10
State‐of‐health estimation of lithium‐ion batteries: A comprehensive literature review from cell to pack levels
Lingzhi Su, Yan Xu, Zhaoyang Dong
Energy Conversion and Economics (2024) Vol. 5, Iss. 4, pp. 224-242
Open Access | Times Cited: 3
Hybrid acceleration techniques for the physics-informed neural networks: a comparative analysis
F. A. Buzaev, Jiexing Gao, Ivan Chuprov, et al.
Machine Learning (2023) Vol. 113, Iss. 6, pp. 3675-3692
Closed Access | Times Cited: 8
Data-driven designs and multi-scale simulations of enhanced ion transport in low-temperature operation for lithium-ion batteries
Hongjun Chang, Yoojin Park, Ju-Hee Kim, et al.
Korean Journal of Chemical Engineering (2023) Vol. 40, Iss. 3, pp. 539-547
Closed Access | Times Cited: 7
A novel multiple training-scale dynamic adaptive cuckoo search optimized long short-term memory neural network and multi-dimensional health indicators acquisition strategy for whole life cycle health evaluation of lithium-ion batteries
Pu Ren, Shunli Wang, Xianpei Chen, et al.
Electrochimica Acta (2022) Vol. 435, pp. 141404-141404
Open Access | Times Cited: 11
A method for estimating lithium-ion battery state of health based on physics-informed machine learning
Guoqing Sun, Yafei Liu, Xuewen Liu
Journal of Power Sources (2024) Vol. 627, pp. 235767-235767
Closed Access | Times Cited: 1
A Physics-Informed Deep Neural Network based beam vibration framework for simulation and parameter identification
Cem Söyleyici, Hakkı Özgür Ünver
Engineering Applications of Artificial Intelligence (2024) Vol. 141, pp. 109804-109804
Closed Access | Times Cited: 1
An Interpretable State of Health Estimation Method for Lithium-Ion Batteries Based on Multi-Category and Multi-Stage Features
Guangzheng Lyu, Heng Zhang, Qiang Miao
(2023)
Closed Access | Times Cited: 2
An Adaptive and Interpretable Soh Estimation Method for Lithium-Ion Batteries Based-On Relaxation Voltage Cross-Scale Features and Multi-Lstm-Rfr2
Guangzheng Lyu, Heng Zhang, Qiang Miao
(2023)
Closed Access | Times Cited: 2
Establishing Second-life Application Suitability Classification Criteria for Retired Batteries using Autoencoder and K-Means Clustering
Marvin A. Radaza, Jayson C. Jueco, Joshua Carl A. Abalorio, et al.
(2024), pp. 146-151
Closed Access
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