Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,707

Finding structure in time

Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212

The development and future of lithium ion batteries

Blomgren, G. E. (2017) · Journal of The Electrochemical Society · doi:10.1149/2.0251701jes · Cited by 1,940

Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326

The environmental impact of Li-Ion batteries and the role of key parameters – A review

Peters, J. F., Baumann, M., Zimmermann, B., Braun, J., & Weil, M. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.08.039 · Cited by 856

Gaussian process regression for forecasting battery state of health

Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.004 · Cited by 732

A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation

Patil, M. A., Tagade, P., Hariharan, K. S., Kolake, S. M., Song, T., Yeo, T., & Doo, S. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.08.119 · Cited by 631

Remaining useful life prediction of lithium-ion battery with unscented particle filter technique

Miao, Q., Xie, L., Cui, H., Liang, W. Z., & PECHT, M. G. (2012) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.004 · Cited by 479

Direct Remaining Useful Life Estimation Based on Support Vector Regression

Khelif, R., Chebel‐Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., & Noureddine Zerhouni, A. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2623260 · Cited by 440

Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

Richardson, R. R., Birkl, C. R., Osborne, M. A., & Howey, D. A. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2794997 · Cited by 407

A Fusion Method Based on EEMD, Pearson Correlation Analysis, Improved LSTM, and Gaussian Function-Trust Region Algorithm for State of Health Prediction of Lithium-Ion Batteries

Peng, X., & Li, Y. (2021) · Journal of Electrochemical Energy Conversion and Storage · doi:10.1115/1.4052093 · Cited by 7

Lithium Battery Life Prediction Based on DBN and LSTM

Ran, B., & Yuan, H. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2433/1/012020 · Cited by 7

Residual life prediction of lithium battery based on hybrid model of WOA–VMD and attention mechanism

Tao, Q., Lv, J., & Wu, J. (2024) · International Journal of Low-Carbon Technologies · doi:10.1093/ijlct/ctae034 · Cited by 7

State of health estimation and remaining useful life prediction for lithium-ion batteries using FBELNN and RCMNN

Lin, Q., Xu, Z., & Lin, C. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-201952 · Cited by 7

Transfer learning LSTM model for battery useful capacity fade prediction

Gupta, A., Sheikh, M. A., Tripathy, Y., & Widanage, W. D. (2021) · International Conference on Mechatronics Technology (ICMT) · doi:10.1109/icmt53429.2021.9687230 · Cited by 7

State of Health Estimation of Lithium‐Ion Batteries Based on Differential Thermal Voltammetry and Improved Gray Wolf Optimizer Optimizing Gaussian Process Regression

Xu, P., Ran, W., Huang, Y., Xiang, Y., Liu, Y., Xiao, K., Xu, C., & Wan, S. (2025) · Energy Technology · doi:10.1002/ente.202400996 · Cited by 6

Battery States Co-estimation Methodology Using Dual Square Root Unscented Kalman Filter

Sahu, S., Dutt, R., & Acharyya, A. (2023) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas46773.2023.10181678 · Cited by 6

A Review of Data-Driven SOH and RUL Estimation for Lithium-ion Batteries

Liu, Q., Zheng, M. L., & Li, P. P. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240526 · Cited by 6

Research on battery SOH estimation method based on electrochemical impedance spectroscopy

Wang, R., Wang, S., Yuhao, W., Guo, Y., Siwen, C., & Jinlei, S. (2024) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc63154.2024.10755197 · Cited by 6

Evaluating the Effectiveness of Boosting and Bagging Ensemble Techniques in Forecasting Lithium‐Ion Battery Useful Life

Sonthalia, A., Femilda Josephin, J. S., Aloui, F., & Varuvel, E. G. (2025) · Energy Storage · doi:10.1002/est2.70118 · Cited by 6

Remaining useful life prediction of lithium-ion battery based on AConvST-LSTM-Net-TL

Li, Z., Li, J., Wang, X., Zhang, C., & Nie, L. (2025) · Physica Scripta · doi:10.1088/1402-4896/adaa35 · Cited by 6

A hybrid temperature distribution monitoring method for Lithium-ion battery module by integrating multi-physics with machine learning

Zhu, W., Lei, F., Liu, J., & Ding, F. (2025) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2025.127278 · Cited by 6

A prediction-based cycle life test optimization method for cross-formula batteries using instance transfer and variable-length-input deep learning model

Ma, J., Zou, X., Sun, L., Cheng, Y., Lü, C., Su, Y., Chong, J., Jin, H., & Lin, Y. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07322-1 · Cited by 6

Elman neural network-based temperature prediction and optimization for lithium-ion batteries

Li, C., Wang, Y., Chen, X., Yu, Y., Zhou, G., Wang, C., & Zhao, W. (2023) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070231174118 · Cited by 6

Novel virtual sample generation using Gibbs Sampling integrated with GRNN for handling small data in soft sensing

Zhu, Q., Zhao, Q.-Q., Xu, Y., & He, Y. (2023) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls58216.2023.10166679 · Cited by 6

Remaining useful life prediction of lithium-ion batteries based on MWASFormer network

Zeng, L., Jiang, Z., & Wang, S. (2025) · Ionics · doi:10.1007/s11581-025-06794-0 · Cited by 5

Research on SOH Prediction Method of New Energy Vehicle Power Battery

Yu, Z., Chen, H., & Wang, C. (2021) · International Conference on Transportation Information and Safety · doi:10.1109/ictis54573.2021.9798578 · Cited by 5

Temporal Attention Mechanism Based Indirect Battery Capacity Prediction Combined with Health Feature Extraction

Chu, F., Shan, C., & Guo, L. (2023) · Electronics · doi:10.3390/electronics12244951 · Cited by 5

Real-Time Predicting the Low-Temperature Performance of WLTC-Based Lithium-Ion Battery Using an LSTM-PF Sequential Ensemble Model

Sim, M.-S., Kim, D.-Y., Yoon, Y., Kang, S., & Baek, J. D. (2024) · IEEE Access · doi:10.1109/access.2024.3419009 · Cited by 5

Advancing Lithium-ion Battery Prognostics: A Novel Deep Learning Framework for Enhanced SOH and RUL Prediction Accuracy

Zraibi, B., Mansouri, M., & Aoud, M. M. E. (2024) · International Conference on Ubiquitous Networking (UNet) · doi:10.1109/unet62310.2024.10794729 · Cited by 5

State of health prediction of lithium-ion batteries based on incremental capacity analysis and adaptive genetic algorithm optimized Elman neural network model

Liu, Z., Wang, H., Zhou, X. J., Chen, H., Duan, H., Liang, K., Chen, B., Cao, Y. T., Wang, W., Yang, D., et al. (2025) · Energy · doi:10.1016/j.energy.2025.137955 · Cited by 5

A novel lithium-ion battery capacity prediction framework based on SVMD-AO-DELM

Ding, G., & Chen, H. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02607-x · Cited by 5

Luminance degradation test and life prediction of LED light at conventional stress using TPWRAM

Zhang, J., & Zhang, Y. (2023) · Journal of Materials Science Materials in Electronics · doi:10.1007/s10854-023-11531-2 · Cited by 5

Edge of Transfer Learning-Based Long Short-Term Memory Neural Networks in the Application of Battery Surface Temperature Prediction for Electric Vehicles

Kumar, P., & Kumar, S. (2024) · IEEE Journal of Emerging and Selected Topics in Industrial Electronics · doi:10.1109/jestie.2024.3356974 · Cited by 5

A fatigue perspective on damage accumulating in lithium-ion batteries under dynamic cycling

Liu, J., Chang, Z., Chen, C., Wen, J., Chen, X., Duan, C., & Wei, Y. (2024) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2024.108773 · Cited by 5

Deep Learning Approaches to Remaining Useful Life Prediction: A Survey

Cummins, L., Killen, B., Kirby, T., Barrett, P., Rahimi, S., & Seale, M. A. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9659965 · Cited by 5

A state-of-the-art review of adaptive, predictive, synergistic, and degradation-aware power variability smoothing techniques in photovoltaic power plants

Abdalla, A. A., Moursi, M. S. E., EL-Fouly, T. H. M., & Hosani, K. H. A. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116687 · Cited by 4

Tiny Long-Short Term Memory Model for Resource-Constrained Prediction of Battery Cycle Life

Chang, Y., Thompson, R., & Hixenbaugh, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82484-5_10 · Cited by 4

Online Fault Tolerant RUL Prediction Strategy for Lithium-Ion Batteries Using Machine Learning

Zraibi, B., Mansouri, M., Okar, C., & Chaoui, H. (2025) · IEEE Access · doi:10.1109/access.2025.3554720 · Cited by 4

Segmented Prediction Model of Lithium-ion Battery Remaining Useful Life Based on LSTM and SVR

Wang, Y., Liu, C., Zhang, X., Deng, Y., Chen, L., Liu, H., Liu, B., & Zhang, Y. (2023) · International Conference on Intelligent Robotics and Control Engineering (IRCE) · doi:10.1109/irce59430.2023.10254780 · Cited by 4

Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

Chen, Z., Wang, N., Jin, W., & Li, D. D. (2024) · Energy Engineering · doi:10.32604/ee.2023.045270 · Cited by 4

Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries

Zhang, Y., Han, Y., Cai, T., Xie, J., & Cheng, S. (2024) · IET Energy Systems Integration · doi:10.1049/esi2.12171 · Cited by 4

A Data-Driven DAE-CNN-BiLSTM-Attention Prediction Model for the State of Health of Lithium-Ion Batteries

Zhang, C., Hu, Y., Huang, D., Fang, J., & Qin, N. (2024) · International Conference on Information Science and Technology · doi:10.1109/icist63249.2024.10805337 · Cited by 4

RUL prediction for lithium-ion batteries using improved-CGD hybrid model

Zraibi, B., Mansouri, M., & Loukili, S. E. (2025) · International Journal of Dynamics and Control · doi:10.1007/s40435-025-01737-z · Cited by 4

Predicting SoH in Lithium-ion Batteries using a Variational Quantum Neural Network

Mutua, A. M., de Fréin, R., & Kimeli, K. (2025) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits65975.2025.11099466 · Cited by 4

Lithium-ion Batteries RUL Prediction Based on Temporal Pattern Attention

Qin, H., Fan, X., Fan, Y., Wang, R., & Tian, F. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2320/1/012005 · Cited by 4

Full Feedback Dynamic Neural Network with Exogenous Inputs for Dynamic Data‐Driven Modeling in Nonlinear Dynamic Power Systems

Zhang, Z., Zhang, Z., Zhao, S., Hong, Z., Huang, S., & Li, Q. (2023) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.23801 · Cited by 4

A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization

Chen, D., Zhang, W., Zhang, C., Sun, B., Chen, H., Yang, S., & Cong, X. (2023) · Batteries · doi:10.3390/batteries9080414 · Cited by 4

Accurate RUL Prediction Based on Sliding Window with Sparse Sampling

Song, C., & Lee, S. (2021) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom51814.2021.9377402 · Cited by 4

A bayesian Mamba model with time windowing for lithium-ion battery state of health Estimation

Wang, H., Huang, Q., & Gao, M. (2025) · Ionics · doi:10.1007/s11581-025-06793-1 · Cited by 3

Lithium-Ion Battery Lifetime Prediction Model Based on a Fusion Expert Network

Meng, Y., Sun, Q., Wang, Z., Yang, Q., Song, Y., Xie, R., Liu, Q., Yang, L., & Ren, F. (2025) · Batteries · doi:10.3390/batteries11120440 · Cited by 3

A lithium-ion battery capacity degradation prediction method based on adaptive multi-scale feature extraction

Lei, Y., & Yu, G. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.238986 · Cited by 3

Remaining useful life prediction of Li-ion batteries using fusion-based data-driven GRU-CNN hybrid model

Sah, B. K., Yadav, A. K., & Verma, O. P. (2026) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-026-07295-3 · Cited by 3

Intelligent Health Management of Lithium-Ion Batteries: A Review and Future Perspectives with Digital Twin Technology

Wang, M., Feng, S., Andriukaitis, D., Li, Z., Chauhan, S., & Vashishtha, G. (2026) · Automotive Innovation · doi:10.1007/s42154-025-00385-5 · Cited by 3

Predicting the operational remaining life of lithium-ion battery with dynamic attention transformer

Varghese, J. J., & Shittu, E. (2025) · Future Batteries · doi:10.1016/j.fub.2025.100075 · Cited by 3

Federated Denoising Transformer-Recurrent Regressor for Remaining Useful Life Prediction of Lithium-Ion Batteries

Yang, J., & Dang, Y. (2025) · ACS Applied Energy Materials · doi:10.1021/acsaem.5c01927 · Cited by 3

iTransformer-LSTM dual-stream architecture for rolling bearing remaining useful life prediction

Chen, Z., Shi, M., Wang, Y., & Chen, L. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04617-3 · Cited by 3

A novel RUL prediction method for Lithium-ion Batteries via hybrid modeling and BAS based particle filter

He, N., Qian, C., & Li, R. (2021) · Chinese Control Conference (CCC) · doi:10.23919/ccc52363.2021.9550743 · Cited by 3

End-of-discharge prediction for satellite lithium-ion battery based on evidential reasoning rule

Zhao, D., Zhou, Z., Zhang, P., Zhang, Y., Qin, H., & Gao, S. (2022) · Open Astronomy · doi:10.1515/astro-2022-0031 · Cited by 3

Wireless Capsule Endoscopic Image Classification using Seeker Optimization Algorithm with Deep Learning Model

Amirthalingam, M., & Ponnusamy, R. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142708 · Cited by 3

An Attention-Based Deep Learning Approach for Lithium-ion Battery Lifespan Prediction: Analysis and Experimental Validation

Darwish, A. (2024) · Information Sciences with Applications · doi:10.61356/j.iswa.2024.2224 · Cited by 3

A Fusion of SVR and Fully Connected Networks for Improved RUL Prediction of Lithium Ion Batteries

Keren Persis, P., Gannamani, S., Prabhu, G. A., Sam Daniel Fenny, A., Sadhikh, M., & Kishore, Y. R. (2024) · Global Conference on Communications and Information Technologies (GCCIT) · doi:10.1109/gccit63234.2024.10862887 · Cited by 3

A review and bibliometric analysis of intelligent techniques for advanced battery state estimation in aviation propulsion systems

Osman, A., Mistarihi, M. Z., Ramadan, M., Ghazal, M. A., & AlKhedher, M. A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106741 · Cited by 3

Research on Battery Life prediction Based on Deep Learning

Cao, Y., Zhao, B., Wang, L., Shen, H., & Hu, J. (2020) · Asia-Pacific Power and Energy Engineering Conference (APPEEC) · doi:10.1109/appeec48164.2020.9220734 · Cited by 3

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Vanem, E., Alnes, Ø. Å., & Lam, J. (2021) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2021.v13i1.2972 · Cited by 3

Transformer Neural Network for Early Battery Capacity Prediction Based on Electrochemical Impedance Spectroscopy

Ning, Z., Venugopal, P., Rietveld, G., & Soeiro, T. B. (2023) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine58569.2023.10405626 · Cited by 3

Tensorflow-based Long-Short term Memory Network for SOH Prediction of a Lithium-ion Cell

Han, D., Kwon, S., Kim, S., Lim, C.-W., & Kin, J. (2019) · IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific) · doi:10.1109/itec-ap.2019.8903704 · Cited by 3

Machine Learning Applied to Battery Prognostics based on Advanced State of Health Estimation

Benlamine, K., & Mesbahi, T. (2022) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc55846.2022.10003444 · Cited by 3

Bimodal decomposition and LSTM network-based joint prediction of reversible capacity and remaining useful life for Li-ion batteries

Yu, S. Q., Zhang, L., Ni, L., & Feng, S. (2025) · Journal of Power Electronics · doi:10.1007/s43236-025-01013-5 · Cited by 2

Attention-based bidirectional LSTM model construction and application for lithium-ion battery state-of-health prediction

An, Z., Ma, J., Du, X., Ding, Y., Zhang, D. X., & Fu, J. (2025) · Ionics · doi:10.1007/s11581-025-06678-3 · Cited by 2

Hybrid Learning Models for Remaining Useful Life Prediction of Lithium-Ion Batteries in Electric Vehicles

Sahdev, G., Agarwal, S., & Bhati, P. (2025) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1334/1/012003 · Cited by 2

A multi-scale temporal fusion network for accurate and uncertainty-aware battery remaining useful life prediction

You, C., Dong, C., Lu, J., Xie, S., Duan, Y., Duan, L., Yan, H., Luo, W., & Chen, S. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.12.040 · Cited by 2

Robust capacity estimation of lithium-ion batteries under domain shift using partial incremental capacity features and transfer learning

Park, S., Son, D., & Kim, S. W. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.121770 · Cited by 2

Deep learning and transfer learning for accurate electrode-level degradation diagnosis in lithium-ion batteries

Xiang, H., Zhang, Y., & Feng, W. (2026) · Next Energy · doi:10.1016/j.nxener.2026.100665 · Cited by 2

Enhancing Predictive Battery Maintenance Through the Use of Explainable Boosting Machine

Jafari, S., & Byun, Y.-C. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-44146-2_6 · Cited by 2

A residual service life prediction of lithium-ion batteries based on decomposition algorithm and fully connected neural network

Zhang, X., Wang, Z., Shen, M., Gong, Q., & Wang, Y. (2024) · Ionics · doi:10.1007/s11581-024-05868-9 · Cited by 2

One-Time Prediction of Battery Capacity Fade Curve under Multiple Fast Charging Strategies

Han, X., Dai, Z., Ren, M., Cui, J., & Shi, Y. (2024) · Batteries · doi:10.3390/batteries10030074 · Cited by 2

Feature Selection and Machine Learning for Reliable SOH Estimation Using Electrochemical Impedance Spectroscopy

Liu, Y., Liu, C., Huang, P., & Lee, C.-Y. (2025) · IEEE Industry Applications Society Annual Meeting (IAS) · doi:10.1109/ias62731.2025.11061655 · Cited by 2

State of Health Estimation for Lithium-Ion Batteries Using Hybrid Signal Decomposition and Deep Feature Fusion

Ren, Z., & Yu, W. (2025) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ae0983 · Cited by 2

Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN

Jalali, R., & Etemadfard, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-70415-5 · Cited by 2

A new method for estimating lithium-ion battery capacity using genetic programming combined model

Yao, H., Jia, X. D., Wang, B., & Guo, B. (2019) · Prognostics and System Health Management Conference · doi:10.1109/phm-qingdao46334.2019.8942970 · Cited by 2

Research on Gray Prediction of Heated Surface Combining Empirical Mode Decomposition and Long Short-term Memory Network

Li, M., Shi, Y., Cui, F., Wen, J., & Zeng, J. (2020) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv50220.2020.9305412 · Cited by 2

Degradation Modeling and Remaining Useful Life Estimation: From Statistical Signal Processing to Deep Learning Models

Asif, A. A., Al-Dulaimi, A., & Mohammadi, A. (2020) · Spectrum Research Repository (Concordia University) · Cited by 2

Attention-based Deep Neural Networks for Battery Discharge Capacity ForecastingPreprint

Zhang, Y., Zou, C., & Chen, X. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.06738 · Cited by 2

Review of Fusion Prognostics for Lithium-Ion Batteries - Current State and Future Challenges

Daniel, N., Stoyanov, S., Bailey, C., & Flynn, D. (2021) · International Spring Seminar on Electronics Technology · doi:10.1109/isse51996.2021.9467644 · Cited by 1

Mathematical analysis of battery data for development of data-driven degradation model

Abbas, M., Cho, I., & Kim, J. (2021) · International Conference on Electrical Machines and Systems (ICEMS) · doi:10.23919/icems52562.2021.9634440 · Cited by 1

Predicting Remaining Useful Life of Lithium-Ion Battery Using Extended Belief Rule Base Model

Yang, L.-H., Qian, B.-Y., Huang, C., Ye, F.-F., Hu, H., & Wu, H. (2023) · International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · doi:10.1109/iske60036.2023.10480946 · Cited by 1

Lithium-Ion battery remaining useful life prediction Using variational Mode Decomposition and Improved Extreme Learning Machine Model

Shen, Y., & Li, D. (2023) · International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) · doi:10.1109/ricai60863.2023.10489726 · Cited by 1

Remaining Useful Life Estimation of Lithium-Ion Batteries Via Hyperparameter Optimized Bi-Long Short-Term Memory Recurrent Neural Networks

Sahay, R., Pugalenthi, K., & Raghavan, N. (2023) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-hangzhou58797.2023.10482631 · Cited by 1

A Comprehensive Degradation Modeling Comparison From Statistical to Artificial Intelligence Models for Curing Oven Chains

Misaii, H., Ponchet-Durupt, A., Vu, H. C., Boudaoud, N., Leduc, P., Xu, Y., & Caracciolo, A. (2025) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.2930 · Cited by 1

Advanced Hybrid Deep Learning Model for Accurate RUL Prediction of Lithium-Ion Batteries

Zraibi, B., Mansouri, M., Lammamri, O., Loukili, S. E., & Said, M. A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-95326-2_19 · Cited by 1

Robust Lithium-ion Battery SOH Estimation via Denoising Autoencoder-CNN-Transformer with Noisy Multi-Feature Input

Xie, S., Chen, D., & Dong, G. (2025) · IEEE China Conference on System Simulation Technology and its Applications (CCSSTA) · doi:10.1109/ieeeconf65522.2025.11137038 · Cited by 1

Lossless Information‐Based Dimensionality Reduction of Comprehensive Features With a Deep Variational Autoencoder Enables Early‐Life Prediction of Lithium‐Ion Batteries

Zhang, L., Zhang, Z., Hou, C., Chen, D., ZHANG, C., Zhu, T., & Zhang, W. (2025) · Carbon Neutralization · doi:10.1002/cnl2.70097 · Cited by 1

A study on the state of health estimation method for lithium batteries based on VMD decomposition and feature collaboration

Cui, Y., & Feng, Y. (2026) · Measurement · doi:10.1016/j.measurement.2026.120338 · Cited by 1

An adaptive timescale decoupled parameter identification equivalent circuit model for lithium-ion battery capacity estimation

Wang, K., Jia, T., Wang, M., Bao, C., Li, H., Yang, H., Cen, K., Yan, J., & Bo, Z. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.122024 · Cited by 1

Physics-informed signal decomposition and data-driven RUL estimation for lithium-ion batteries

Fan, Y., Xu, K., Zhu, K., & Yu, X. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6297 · Cited by 1

A hybrid deep learning model for robust and data-efficient lithium-ion battery remaining useful life prediction

Xu, M., Xing, S., Hong, J., Tian, X., Huang, B., Jin, H., & Li, J. (2026) · Journal of Materials Informatics · doi:10.20517/jmi.2025.80 · Cited by 1

Intelligent digital twin for real-time remaining useful life prediction of lithium-ion batteries with cumulative incremental learning and ensemble strategy

Haghighi, M., Mohammadi, M., & Allahbakhshi, M. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.111961 · Cited by 1

Temperature prediction of lithium-ion batteries based on multiphysics modeling and artificial neural networks

Zhou, W., Song, Z., Fang, J., Liu, X., & Yan, Y. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123679 · Cited by 2

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