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Quanxi Guo, Francesco Grimaccia, Alessandro Niccolai, Renxin Tu
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High-energy lithium-ion batteries: recent progress and a promising future in applications
10.1002/eem2.12450 · 2023
Self-discharge losses in lithium-ion cells
10.1109/maes.2004.1269687 · 2004
Research on a fast detection method of self-discharge of lithium battery
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A method to estimate battery SOH indicators based on vehicle operating data only
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Online state-of-health prediction for Li-ion battery using partial charging segment based on support vector machine
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SOH prediction of lithium-ion batteries based on least squares support vector machine error compensation model
10.1007/s43236-021-00307-8 · 2021
Random forest regression for online capacity prediction of lithium-ion batteries
10.1016/j.apenergy.2018.09.182 · 2018
An optimized random forest regression model for li-ion battery prognostics and health management
10.3390/batteries9060332 · 2023
A data-driven method for state of health prediction of lithium-ion batteries in a unified framework
10.1016/j.est.2022.104371 · 2022
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Confidence 0%
A data denoising method based on the ICEEMDAN-EA algorithm for SOH estimation
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Model complexity of deep learning: a survey
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SOH evaluation and RUL prediction of lithium-ion batteries based on MC-CNN-TimesNet model
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An improved CNN-LSTM model-based state-of-health prediction approach for lithium-ion batteries
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Battery state-of-health prediction based on feature extraction and a variational mode decomposition–temporal convolutional network–bidirectional long short-term memory network–self-attention model
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State of health estimation of lithium-ion batteries based on feature optimization and data-driven models
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Uncertainty-oriented collaborative learning for on-orbit state-of-health prediction of satellite lithium-ion batteries considering multi-operating conditions
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A hybrid machine learning framework for joint SOC and SOH prediction of lithium-ion batteries assisted with fiber sensor measurements
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A unified GPR model based on transfer learning for SOH prediction of lithium-ion batteries
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Battery health prediction based on multidomain transfer learning
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Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries
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Analysis and prediction of battery aging modes based on transfer learning
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Meta-learning driven small-sample lithium-ion battery state of health prediction based on Kolmogorov–Arnold network and long short-term memory network
10.1016/j.est.2026.122207 · 2026
Towards real-world state of health prediction, part 1: cell-level method using lithium-ion battery laboratory data
10.1016/j.etran.2024.100338 · 2024
Combined meta-learning with CNN-LSTM algorithms for state-of-health prediction of lithium-ion battery
10.1109/tpel.2024.3398010 · 2024
Domain similarity meta-learning for lithium-ion battery state-of-health estimation of spacecraft systems
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A brief review of hypernetworks in deep learning
10.1007/s10462-024-10862-8 · 2024
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
10.1038/s41467-024-48779-z · 2024
Data-driven prediction of battery cycle life before capacity degradation
10.1038/s41560-019-0356-8 · 2019
Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method
10.1016/j.jpowsour.2011.08.040 · 2011
Advances and challenges in meta-learning: a technical review
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Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method
10.1016/j.jpowsour.2011.08.040 · doi-reference
Data-driven prediction of battery cycle life before capacity degradation
10.1038/s41560-019-0356-8 · doi-reference
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
10.1038/s41467-024-48779-z · doi-reference
A brief review of hypernetworks in deep learning
10.1007/s10462-024-10862-8 · doi-reference
Domain similarity meta-learning for lithium-ion battery state-of-health estimation of spacecraft systems
10.1109/taes.2025.3540806 · doi-reference
Combined meta-learning with CNN-LSTM algorithms for state-of-health prediction of lithium-ion battery
10.1109/tpel.2024.3398010 · doi-reference
Towards real-world state of health prediction, part 1: cell-level method using lithium-ion battery laboratory data
10.1016/j.etran.2024.100338 · doi-reference
Meta-learning driven small-sample lithium-ion battery state of health prediction based on Kolmogorov–Arnold network and long short-term memory network
10.1016/j.est.2026.122207 · doi-reference
Analysis and prediction of battery aging modes based on transfer learning
10.1016/j.apenergy.2023.122330 · doi-reference
Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries
10.1109/tie.2019.2946551 · doi-reference
Battery health prediction based on multidomain transfer learning
10.1109/tpel.2023.3346335 · doi-reference
A unified GPR model based on transfer learning for SOH prediction of lithium-ion batteries
10.1016/j.jprocont.2024.103337 · doi-reference
Lithium-ion battery state of health estimation based on CNN-LSTM-attention-FVIM algorithm and fusion of multiple health features
10.3390/app15137555 · doi-reference
Transfer learning-motivated intelligent fault diagnosis designs: a survey, insights, and perspectives
10.1109/tnnls.2023.3290974 · doi-reference
Deep convolutional neural models for picture-quality prediction: challenges and solutions to data-driven image quality assessment
10.1109/msp.2017.2736018 · doi-reference
Predicting the state of charge and health of batteries using data-driven machine learning
10.1038/s42256-020-0156-7 · doi-reference
A hybrid machine learning framework for joint SOC and SOH prediction of lithium-ion batteries assisted with fiber sensor measurements
10.1016/j.apenergy.2022.119787 · doi-reference
Uncertainty-oriented collaborative learning for on-orbit state-of-health prediction of satellite lithium-ion batteries considering multi-operating conditions
10.1016/j.apenergy.2026.127457 · doi-reference
State of health estimation of lithium-ion batteries based on feature optimization and data-driven models
10.1016/j.energy.2025.134578 · doi-reference
Battery state-of-health prediction based on feature extraction and a variational mode decomposition–temporal convolutional network–bidirectional long short-term memory network–self-attention model
10.1016/j.est.2026.121629 · doi-reference
An improved CNN-LSTM model-based state-of-health prediction approach for lithium-ion batteries
10.1016/j.energy.2023.127585 · doi-reference
SOH evaluation and RUL prediction of lithium-ion batteries based on MC-CNN-TimesNet model
10.1016/j.ress.2025.111125 · doi-reference
Recent advances in deep learning
10.1007/s13042-020-01096-5 · doi-reference
Model complexity of deep learning: a survey
10.1007/s10115-021-01605-0 · doi-reference
A data denoising method based on the ICEEMDAN-EA algorithm for SOH estimation
10.1016/j.jpowsour.2026.240448 · doi-reference
A data-driven method for state of health prediction of lithium-ion batteries in a unified framework
10.1016/j.est.2022.104371 · doi-reference
An optimized random forest regression model for li-ion battery prognostics and health management
10.3390/batteries9060332 · doi-reference
Random forest regression for online capacity prediction of lithium-ion batteries
10.1016/j.apenergy.2018.09.182 · doi-reference
SOH prediction of lithium-ion batteries based on least squares support vector machine error compensation model
10.1007/s43236-021-00307-8 · doi-reference
Online state-of-health prediction for Li-ion battery using partial charging segment based on support vector machine
10.1109/tvt.2019.2927120 · doi-reference
A method to estimate battery SOH indicators based on vehicle operating data only
10.1016/j.energy.2021.120235 · doi-reference
Research on a fast detection method of self-discharge of lithium battery
10.1016/j.est.2022.105431 · doi-reference
Self-discharge losses in lithium-ion cells
10.1109/maes.2004.1269687 · doi-reference
High-energy lithium-ion batteries: recent progress and a promising future in applications
10.1002/eem2.12450 · doi-reference