Specialized deep neural networks for battery health prognostics: Opportunities and challenges

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

Generative adversarial networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384

Physics-informed machine learning

Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00314-5 · Cited by 8,718

Extracting and composing robust features with denoising autoencoders

Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436

Domain Adaptation via Transfer Component Analysis

Pan, S. J., Tsang, I. W.-H., Kwok, J. T.-Y., & Yang, Q. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2091281 · Cited by 4,809

Deep Reinforcement Learning: A Brief Survey

Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733

Generative Adversarial Networks: An Overview

Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540

Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.03.012 · Cited by 3,291

Data-driven prediction of battery cycle life before capacity degradation

Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228

Battery Safety: Machine Learning-Based Prognostics

Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216

Battery safety: Fault diagnosis from laboratory to real world

Zhao, J., Feng, X., Tran, M., Fowler, M. W., Ouyang, M., & Burke, A. F. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234111 · Cited by 229

Battery Fault Diagnosis and Failure Prognosis for Electric Vehicles Using Spatio-Temporal Transformer Networks

Zhao, J., Feng, X., Wang, J., Lian, Y., Ouyang, M., & Burke, A. F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121949 · Cited by 152

Artificial Intelligence in Gas Sensing: A Review

Chowdhury, M. A. Z., & Oehlschlaeger, M. A. (2025) · ACS Sensors · doi:10.1021/acssensors.4c02272 · Cited by 150

Rapid health estimation of in-service battery packs based on limited labels and domain adaptation

Deng, Z., Xu, L., Liu, H., Hu, X.-S., Wang, B., & Zhou, J.-J. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.10.056 · Cited by 110

Exploring impedance spectrum for lithium-ion batteries diagnosis and prognosis: A comprehensive review

Du, X.-H., Meng, J.-H., Amirat, Y., Gao, F., & Benbouzid, M. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.04.005 · Cited by 101

Artificial intelligence-driven real-world battery diagnostics

Zhao, J., Qu, X., Wu, Y., Fowler, M. W., & Burke, A. F. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100419 · Cited by 113

Insights and reviews on battery lifetime prediction from research to practice

Qu, X., Shi, D., Zhao, J., Tran, M., Wang, Z., Fowler, M. W., Lian, Y., & Burke, A. F. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.03.013 · Cited by 101

Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Qi, Q., Liu, W., Deng, Z., Li, J., Song, Z., & Hu, X. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.01.047 · Cited by 83

A Comparative Analysis of Generative Artificial Intelligence Tools for Natural Language Processing

Iorliam, A., & Ingio, J. A. (2024) · Journal of Computing Theories and Applications · doi:10.62411/jcta.9447 · Cited by 98

Optimized XGBoost modeling for accurate battery capacity degradation prediction

Jafari, S., Yang, J.-H., & Byun, Y.-C. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102786 · Cited by 74

Battery health diagnostics: Bridging the gap between academia and industry

Wang, Z., Shi, D., Zhao, J., Chu, Z., Guo, D., Eze, C., Qu, X., Lian, Y., & Burke, A. F. (2023) · eTransportation · doi:10.1016/j.etran.2023.100309 · Cited by 74

Predictive pretrained transformer (PPT) for real-time battery health diagnostics

Zhao, J., Wang, Z., Wu, Y., & Burke, A. F. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124746 · Cited by 79

State estimation of lithium-ion batteries via physics-machine learning combined methods: A methodological review and future perspectives

Yu, H., Zhang, H., Zhang, Z., & Yang, S. (2025) · eTransportation · doi:10.1016/j.etran.2025.100420 · Cited by 75

Big data-driven prognostics and health management of lithium-ion batteries:A review

Chen, K., Luo, Y., Long, Z., Li, Y., Nie, G., Liu, K., Xin, D., Gao, G., & Wu, G. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115522 · Cited by 69

State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review

Sylvestrin, G. R., Maciel, J. N., Amorim, M. L. M., Carmo, J. P., Afonso, J. A., Lopes, S. F., & Ando, O. H. (2025) · Energies · doi:10.3390/en18030746 · Cited by 68

Opportunities and challenges in transformer neural networks for battery state estimation: Charge, health, lifetime, and safety

Zhao, J., Han, X., Wu, Y., Wang, Z., & Burke, A. F. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.11.011 · Cited by 66

AI and machine learning in climate change research: A review of predictive models and environmental impact

Hamdan, A., Ibekwe, K. I., Etukudoh, E. A., Umoh, A. A., & Ilojianya, V. I. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.1.0257 · Cited by 79

Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models

Moulaei, K., Afshari, L., Moulaei, R., Sabet, B., Mousavi, S. M., & Afrash, M. R. (2024) · Scientific Reports · doi:10.1038/s41598-024-82931-5 · Cited by 55

Battery state of health estimation under fast charging via deep transfer learning

Zhao, J., Li, D., Li, Y., Shi, D., Nan, J., & Burke, A. F. (2025) · iScience · doi:10.1016/j.isci.2025.112235 · Cited by 53

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Madani, S. S., Ziebert, C., Vahdatkhah, P., & Sadrnezhaad, S. K. (2024) · Batteries · doi:10.3390/batteries10060204 · Cited by 57

Specialized convolutional transformer networks for estimating battery health via transfer learning

Zhao, J., & Wang, Z. (2024) · Energy storage materials · doi:10.1016/j.ensm.2024.103668 · Cited by 52

Next-generation battery safety management: Machine learning assisted life-time prediction and performance enhancement

Wang, L., Qiu, Y., Yuan, W., Tian, Y., & Zhou, Z. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.05.065 · Cited by 46

A Review of Battery Energy Storage Optimization in the Built Environment

Coccato, S., Barhmi, K., Lampropoulos, I., Golroodbari, S., & van Sark, W. G. J. H. M. (2025) · Batteries · doi:10.3390/batteries11050179 · Cited by 46

Cross-Material Battery Capacity Estimation Using Hybrid-Model Fusion Transfer Learning

Zhao, J., Qu, X., Han, X., Wu, Y., & Burke, A. F. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235674 · Cited by 45

Feature selection strategy optimization for lithium-ion battery state of health estimation under impedance uncertainties

Du, X., Meng, J., Amirat, Y., Gao, F., & Benbouzid, M. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.09.032 · Cited by 47

Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data

Nakano, K., Vögler, S., & Tanaka, K. (2024) · Advances in Applied Energy · doi:10.1016/j.adapen.2024.100188 · Cited by 47

Fault prognosis of Li-ion batteries in electric vehicles: Recent progress, challenges and prospects

Li, H., Kaleem, M. B., Liu, K., Wu, Y., Liu, W., & Peng, Q. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116002 · Cited by 48

Deep machine learning approaches for battery health monitoring

Singh, S. K., & Budarapu, P. R. (2024) · Energy · doi:10.1016/j.energy.2024.131540 · Cited by 46

A generic fusion framework integrating deep learning and Kalman filter for state of charge estimation of lithium-ion batteries: Analysis and comparison

Yu, H., Lu, H., Zhang, Z., & Yang, L. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235493 · Cited by 43

Powering Future Advancements and Applications of Battery Energy Storage Systems Across Different Scales

Dong, Z. Y., Tao, Y., Lai, S., Wang, T., & Zhang, Z. (2025) · Energy storage and applications · doi:10.3390/esa2010001 · Cited by 43

Battery engineering safety technologies (BEST): M5 framework of mechanisms, modes, metrics, modeling, and mitigation

Zhao, J., Lv, Z., Li, D., Feng, X., Wang, Z., Wu, Y., Shi, D., Fowler, M. W., & Burke, A. F. (2024) · eTransportation · doi:10.1016/j.etran.2024.100364 · Cited by 45

Physics-informed battery degradation prediction: Forecasting charging curves using one-cycle data

Tang, A., Xu, Y., Tian, J., Shu, X., & Yu, Q. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.10.018 · Cited by 37

Resource-efficient artificial intelligence for battery capacity estimation using convolutional FlashAttention fusion networks

Lv, Z., & Zhao, J. (2024) · eTransportation · doi:10.1016/j.etran.2024.100383 · Cited by 37

Physics-informed neural networks for small sample state of health estimation of lithium-ion batteries

Chen, L., Chang, C., Liu, X., Jiang, J., Jiang, Y., & Tian, A. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116559 · Cited by 35

Optimizing deep learning predictive models: A comprehensive review of RNN and its variant architectures

Adekunle, A. A., Fofana, I., Picher, P., Rodriguez-Celis, E. M., Arroyo-Fernàndez, O. H., & Zemouri, R. M. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114015 · Cited by 34

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Yang, S., Zhang, C., Chen, H., Wang, J., Chen, D., Zhang, L., & Zhang, W. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.11.040 · Cited by 33

TransRUL: A Transformer-Based Multihead Attention Model for Enhanced Prediction of Battery Remaining Useful Life

Saleem, U., Liu, W., Riaz, S., Li, W., Hussain, G. A., Rashid, Z., & Arfeen, Z. A. (2024) · Energies · doi:10.3390/en17163976 · Cited by 32

Vehicle-cloud-collaborated prognosis and health management for lithium-ion batteries: Framework, technics and perspective

Zhou, S., Zichao, G., Honglei, D., Zhou, X., Yifan, Z., Ma, T., Ming, H., Yubo, L., Fei, C., & Shichun, Y. (2024) · Energy storage materials · doi:10.1016/j.ensm.2024.103531 · Cited by 31

A Systematic Review and Meta-Analysis of Model Predictive Control in Microgrids: Moving Beyond Traditional Methods

Yaghoubi, E., Yaghoubi, E., Maghami, M. R., Rahebi, J., Jahromi, M. Z., Ghadami, R., & Yusupov, Z. (2025) · Processes · doi:10.3390/pr13072197 · Cited by 33

AI-augmented electrochemical model for lithium-ion battery: Recent advances and perspectives

Fu, J., Song, Z., Meng, J., Guo, J., Yang, K., Liu, W., & Huan, L. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.10.008 · Cited by 29

Synergistic Integration of Digital Twins and Neural Networks for Advancing Optimization in the Construction Industry: A Comprehensive Review

Borovkov, A. I., Vafaeva, K. M., Vatin, N. I., & Поняева, И. (2024) · Construction Materials and Products · doi:10.58224/2618-7183-2024-7-4-7 · Cited by 29

State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model

Zhang, Y., Wang, A., Zhang, C., He, P., Shao, K., Cheng, K., & Zhou, Y. (2025) · Batteries · doi:10.3390/batteries11060217 · Cited by 25

Accurate estimation of state of health for lithium-ion batteries based on Pyraformer and TimeGAN data augmentation

Liu, X., Yang, K., Zou, B., Zhang, X., Bao, G., Ma, B., Zhang, L., & Tan, R. Q. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.236722 · Cited by 19

Battery pack capacity prediction using deep learning and data compression technique: A method for real-world vehicles

Yang, Y., Yang, J., Wu, X., Fu, L., Gao, X., Xie, X., & Ouyang, Q. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.02.058 · Cited by 18

A comprehensive review of the dairy pasteurization process using machine learning models

Singh, P., Pandey, S., & Manik, S. (2024) · Food Control · doi:10.1016/j.foodcont.2024.110574 · Cited by 17

Managing Battery Performance Degradation Using Physics-Informed Learning Scheme for Multiple Health Indicators

Qin, L., Sun, T., Sun, X., & Xia, W. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3525742 · Cited by 16

A semi-supervised learning strategy for lithium-ion battery capacity estimation with limited impedance data

Li, Y., He, Z., Ye, M., Wang, Q., Lian, G., Sun, Y., & Wei, M. (2025) · Energy · doi:10.1016/j.energy.2025.135129 · Cited by 16

Vehicle choice modeling for emerging zero-emission light-duty vehicle markets in California

Burke, A. F., Zhao, J., Miller, M. R., & Fulton, L. M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32823 · Cited by 15

Towards artificial intelligence-enabled autonomous battery prognostics and management

Shi, D., Cai, M., Che, Y., Xie, L., & Zhao, J. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.09.081 · Cited by 16

Industrial Battery State-of-Health Estimation with Incomplete Limited Data Towards Second-Life Applications

Yang, S., Ling, L., Li, X., Han, J., & Tong, S. (2024) · Journal of Dynamics Monitoring and Diagnostics · doi:10.37965/jdmd.2024.562 · Cited by 14

A transformer-enhanced framework for lithium-ion battery capacity estimation using limited imaginary impedance feature

Liu, R., Zhang, D., Wang, L., Mi, C. C., Bauer, P., & Qin, Z. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116313 · Cited by 13

Robust Online Estimation of State of Health for Lithium-Ion Batteries Based on Capacities under Dynamical Operation Conditions

Wu, X., Chen, J., Tang, H., Xu, K., Shao, M., & Long, Y. (2024) · Batteries · doi:10.3390/batteries10070219 · Cited by 13

Artificial intelligence in disaster management: achievements, challenges, and prospects

Xu, J., Nair, D. J., & Waller, S. T. (2025) · Natural Hazards · doi:10.1007/s11069-025-07667-5 · Cited by 14

Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning

Foong, L. K., Blažek, V., Prokop, L., Mišák, S., Atamurotov, F., & Khalilpoor, N. (2024) · Engineering Applications of Computational Fluid Mechanics · doi:10.1080/19942060.2024.2391988 · Cited by 11

GenAI for Scientific Discovery in Electrochemical Energy Storage: State‐of‐the‐Art and Perspectives from Nano‐ and Micro‐Scale

Li, S., & You, F. (2024) · Small · doi:10.1002/smll.202406153 · Cited by 11

Revolutionizing Content Creation and Curriculum Development Through Generative AI

Singha, S., & Singha, R. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1351-0.ch013 · Cited by 11

A transferable deep learning framework for cross-chemistry and cross-scale state of health estimation in lithium-ion batteries

Pang, H., Qu, X., Chen, K., Yan, X., Burke, A. F., & Zhao, J. (2026) · Energy · doi:10.1016/j.energy.2026.141079 · Cited by 10

Challenges and prospects in real-world battery status prediction within Industry 4.0

Qu, X., Zhao, J., Pang, H., Fowler, M. W., & Burke, A. F. (2025) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2025.100298 · Cited by 8

A Systematic Review of Prognostics and Health Management in Mobility Batteries

Kumar, P., Tanveer, M., Park, K. T., Raouf, I., & Kim, H. S. (2025) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-025-00751-y · Cited by 9

Large language models for battery prognostics

Zhao, J., Wang, Y., Cai, M., Xie, L., & Wang, H. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.11.021 · Cited by 8

New periodic wave solution for time-fractional perturbed nonlinear Schrödinger equation arising in optical fiber via newly extended mapping scheme

Ouahid, L., Abdou, M. A., Alshahrani, D. O., & Akgül, A. (2025) · Optical Review · doi:10.1007/s10043-025-00994-9 · Cited by 7

Generative data augmentation for improving state estimation and prognostics in lithium-ion batteries: Advances, Challenges, and Future directions

Sifat, M. S. I., Sawda, U., Kabir, M. A., Ahmed, M. U., Lipu, M. S. H., & Islam, M. M. M. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.121006 · Cited by 8

Optimizing battery deployment: Aging trajectory prediction enabling homogenous performance grouping

Wang, S., Gao, F., Ma, Z., Tian, H., & Zhang, Y. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.09.012 · Cited by 6

Dynamical study of optical soliton solutions of time-fractional perturbed model in ultrafast optical fibers

Ouahid, L., Nazar, N. M., Abdou, M. A., Alshahrani, M. D., Abdel-Baset, A. M., Emara, M. H., Kumar, S., & Hamid, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-18740-1 · Cited by 6

Harnessing artificial intelligence for food security in a changing climate

Bhairo‐Beekhoo, P., & Maharaj, R. (2025) · Discover Food · doi:10.1007/s44187-025-00609-x · Cited by 9

Convergence of Computational Materials Science and AI for Next-Generation Energy Storage Materials

Pan, X., Xie, Y., Li, C., He, Y., Zhang, Y., Wang, Y., Li, Z., Peng, P., & Wang, J. (2025) · Journal of Electronic Materials · doi:10.1007/s11664-025-12511-4 · Cited by 6

Coupling Data‐Driven and Reinforcement Learning for Material Development and Device Management in Batteries

He, Z., Wang, Z., Dong, Y., Viktorovich, K. A., Hao, S., Yang, C., Liu, Y., & Dong, L. (2026) · Advanced Materials · doi:10.1002/adma.72332 · Cited by 6

Multi-Tasks Joint Network for Anomaly Diagnosis and Inconsistent Identification of VRLA Battery in Large Data Center

Ye, Z., Yue, S., Yang, P., Zhou, R., & Yu, J. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3534404 · Cited by 6

Artificial Intelligence-Driven Electric Vehicle Battery Lifetime Diagnostics

Zhao, J., & Burke, A. F. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1009354 · Cited by 6

Intelligent Diagnostic System for Mechanical Fault Detection Using Deep Learning

Hussain, S. J., Rupa Devi, B., Ellapalli, E. A. A. S., Lakshmi, H. N., Rangaswamy, K., & Karudumpa, S. (2025) · Journal of Machine and Computing · doi:10.53759/7669/jmc202505065 · Cited by 5

A novel parallel global-local feature enhancement network for remaining useful life prediction of lithium-ion batteries

Wang, P., Cheng, L., Gao, Y., & Mao, T. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118291 · Cited by 4

Relaxation voltage-based dual-stream transfer attention network for state of health estimation of lithium-ion batteries

Wei, Z., Wu, M.-W., Ju, W., He, Q., Shen, Z., & Zhao, Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119917 · Cited by 4

CNN-based prediction of multi-variables for lithium-ion batteries optimized with TinyML and deployed on edge devices

Weng, Y., Guan, W., & Ababei, C. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119508 · Cited by 4

A physics-constrained multi-scale cross-channel attention network for real-world battery state of health prediction

Ren, Z., Ruichao, W. E. I., Huang, S., He, Y., Liang, S., Deng, Z., & Gao, J. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.120562 · Cited by 4

Machine Learning Assisted Development of COFs Materials as Solid Electrolytes for Lithium-Ion Batteries—A Mini Review

Xu, W., Sang, J., Gong, Q., Lin, W., Lin, Z., Mushtaq, F., Mushtaq, H., Hong, Z., & Zhao, H. (2026) · World Electric Vehicle Journal · doi:10.3390/wevj17030113 · Cited by 4

Two-stage prediction–correction with residual compensation: A framework for robust state of charge estimation under cross-temperature conditions

Fan, C., Liu, Y., Liu, K., Li, M., Turgun, Y., & Peng, Q. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123275 · Cited by 4

On nonlinear wave structures, stability analysis and modulation instability of the time fractional perturbed dynamical model in ultrafast fibers

Alhefthi, R. K., Tariq, K. U.-H., & Kazmi, S. M. R. (2024) · Optical and Quantum Electronics · doi:10.1007/s11082-024-06432-5 · Cited by 4

Leveraging the knee point: Boosting remaining useful life prediction accuracy for lithium-ion batteries with virtual-enhanced normalizing flow

Zhang, B., Leng, M., Hu, C., Pei, H., Wang, Z., Li, C., Wang, L., & He, X. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.07.007 · Cited by 4

Battery health estimation using early charging profiles and a hybrid entropy framework enhanced based on exponential trigonometric optimization

Dai, H., Shangguan, Z., Zhu, L., Yu, H., Zhang, Y., & Lin, H. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.238357 · Cited by 4

Battery System Fault Detection: A Data-Driven Aggregation and Augmentation Strategy

Zhang, Z., Zhang, D., Li, D., Liu, Y., & Yang, J. (2025) · IEEE Access · doi:10.1109/access.2025.3574787 · Cited by 4

CBATE-Net: An Accurate Battery Capacity and State-of-Health (SoH) Estimation Tool for Energy Storage Systems

Rehman, F. U., Buccella, C., & Cecati, C. (2025) · Energies · doi:10.3390/en18205533 · Cited by 3

Memory-augmented prototypical transfer for cross-scenario battery health prognostics

Wu, J., Liu, Z., Wang, H., Sun, N., Rong, Q., Li, S., Chen, S., & He, X. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.238781 · Cited by 3

Semi-supervised learning with physicochemical health indicators using an Encoder-Gaussian process framework for battery state of health estimation under dynamic discharge conditions

Benoit, A., Asef, P., Yuan, H. R., & Jafari, R. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100748 · Cited by 3

Advanced Seismic Data Analysis: Comparative study of Machine Learning and Deep Learning for Data Prediction and Understanding

Airlangga, G. (2024) · Brilliance Research of Artificial Intelligence · doi:10.47709/brilliance.v3i2.3501 · Cited by 3

Enhancing Stock Market Prediction Accuracy Through Advanced Metaheuristic Techniques and Macro-Economic Factors Integration

Barua, U., Pant, P., & Miah, M. H. (2024) · International Conference on Advancement in Computation & Computer Technologies (InCACCT) · doi:10.1109/incacct61598.2024.10551013 · Cited by 3

Analyzing the impact of legal education pedagogy on students’ understanding of complex legal concepts

Song, H. (2025) · International Journal of Inclusive Education · doi:10.1080/13603116.2025.2555392 · Cited by 2

Enhancing degradation trend prediction of lithium-ion battery capacity in complex aging scenarios: A Bayesian-optimized hybrid architecture combining local and global feature learning

Yin, C., Fei, B., Yao, J., Wu, Y., Xu, Z., Chen, J., Wan, L., Ge, X., Liu, Q., & Ye, D. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00428-1 · Cited by 2

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