Contact and support
Need help, have a question, or want to contact the ResearchHub team?
© 2026 ResearchHub. Built for responsible scholarly connection.
Privacy-aware community infrastructureResearch graph
References from Early-cycle life prediction of mechanically damaged lithium-ion batteries via multi-physics features and machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Addressing the safety of next-generation batteries
10.1038/s41586-025-09358-4 · 2025 · External reference
Lithium-ion battery end of life prediction based on the decelerating aging point
10.1016/j.apenergy.2025.126692 · 2025 · External reference
Revealing failure mechanisms of lithium-ion batteries under dynamic three-point bending: experiments and simulations
2026 · External reference
Multi-stress accelerated aging for cycle life evaluation of high-capacity, long-life Lithium Iron phosphate batteries
10.1016/j.apenergy.2025.127126 · 2026 · External reference
Rate-dependent damage and failure behavior of lithium-ion battery electrodes
10.1016/j.engfracmech.2024.110143 · 2024 · External reference
Impact of external short circuit on lithium-ion batteries: a post-mortem case study
10.1039/d5ta00705d · 2025 · External reference
Effect of external pressure and internal stress on battery performance and lifespan
10.1016/j.ensm.2022.07.034 · 2022 · External reference
Battery health prediction under data scarcity: a cross-domain physics-informed 5-shot framework with GRU-transformer
10.1016/j.apenergy.2025.127012 · 2026 · External reference
Progressive degradation behavior and mechanism of lithium-ion batteries subjected to minor deformation damage
10.1016/j.est.2024.113992 · 2024 · External reference
Enhancing mechanical reliability and safety performance of a battery pack system for electric vehicles: a review
10.1016/j.etran.2025.100469 · 2025 · External reference
Pressure-induced capacity recovery and performance enhancements in LTO/NMC-LCO batteries
10.1002/adfm.202419229 · 2025 · External reference
Electrochemo-mechanical-thermal dynamics of internal short circuits in batteries
2026 · External reference
Toward the performance evolution of lithium-ion battery upon impact loading
10.1016/j.electacta.2022.141192 · 2022 · External reference
Multiaxial failure characterization and short-circuit prediction of large-format prismatic lithium-ion batteries
10.1016/j.apenergy.2025.126963 · 2026 · External reference
Electrochemical-mechanical understanding of the accelerated degradation of lithium-ion batteries caused by mechanical stress
10.1016/j.energy.2025.137420 · 2025 · External reference
SOH estimation framework for batteriesconsidering label normalization and feature stability under real-world data
10.1016/j.apenergy.2025.127023 · 2026 · External reference
State-of-health estimation and knee point identification of lithium-ion battery based on data-driven and mechanism model
10.1016/j.apenergy.2025.125539 · 2025 · External reference
An electrochemical-thermal coupling model for lithium-ion battery state-of-charge estimation with improve dual particle filter framework
10.1016/j.est.2024.111473 · 2024 · External reference
Physics-based lifetime modeling and parameter identification of lithium-ion batteries under various degradation conditions
2025 · External reference
Development of an empirical aging model for Li-ion batteries and application to assess the impact of vehicle-to-grid strategies on battery lifetime
10.1016/j.apenergy.2016.03.119 · 2016 · External reference
An ensemble model for predicting the remaining useful performance of lithium-ion batteries
2013 · External reference
Remaining useful life prediction of Lithium-ion batteries based on wiener process under time-varying temperature condition
10.1016/j.ress.2021.107675 · 2021 · External reference
Lithium battery aging model based on Dakin’s degradation approach
10.1016/j.jpowsour.2016.06.036 · 2016 · External reference
A physics-based aging model for lithium-ion battery with coupled chemical/mechanical degradation mechanisms
10.1016/j.electacta.2021.139133 · 2021 · External reference
Machine learning pipeline for battery state-of-health estimation
10.1038/s42256-021-00312-3 · 2021 · External reference
Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
10.1039/d2ee03019e · 2023 · External reference
Gaussian process regression for forecasting battery state of health
10.1016/j.jpowsour.2017.05.004 · 2017 · External reference
Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells
10.1016/j.egyai.2020.100006 · 2020 · External reference
Early prediction of battery lifetime based on graphical features and convolutional neural networks
10.1016/j.apenergy.2023.122048 · 2024 · External reference
High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with gaussian process regression
10.1016/j.jpowsour.2023.232737 · 2023 · External reference
Remaining useful life prediction of Lithium-ion batteries based on health Indicator and gaussian process regression model
10.1109/access.2019.2905740 · 2019 · External reference
Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning
10.1039/d4ee03839h · 2025 · External reference
Lithium-ion batteries remaining useful life prediction based on BLS-RVM
10.1016/j.energy.2021.121269 · 2021 · External reference
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
10.1038/s41467-024-48779-z · 2024 · External reference
Early-cycle prediction of battery aging onset across chemistries
10.1016/j.jechem.2025.01.035 · 2025 · External reference
Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation
10.1016/j.apenergy.2022.120204 · 2022 · External reference
Early-stage lifetime prediction for lithium-ion batteries: a deep learning framework jointly considering machine-learned and handcrafted data features
10.1016/j.est.2022.104936 · 2022 · External reference
Capacity estimation and knee point prediction using electrochemical impedance spectroscopy for lithium metal battery degradation via machine learning
10.1002/advs.202502336 · 2025 · External reference
Data-driven prediction of battery cycle life before capacity degradation
10.1038/s41560-019-0356-8 · 2019 · External reference
Systematic feature design for cycle life prediction of lithium-ion batteries during formation
10.1016/j.joule.2025.101884 · 2025 · External reference
Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning
10.1038/s42256-024-00972-x · 2025 · External reference
Comprehensive investigation on the durability and safety performances of lithium-ion batteries under slight mechanical deformation
10.1016/j.est.2023.107450 · 2023 · External reference
Aging trajectory prediction of lithium-ion batteries based on mechanical-electrical features via nonlinear autoregressive and regression neural networks
10.1016/j.est.2024.114696 · 2025 · External reference
Regression random machines: an ensemble support vector regression model with free kernel choice
10.1016/j.eswa.2022.117107 · 2022 · External reference
Intelligent data analysis using optimized support vector machine based data mining approach for tourism industry
10.1145/3494566 · 2022 · External reference
A new classification scheme based on extended Kalman filter and support vector machine
10.1016/j.epsr.2022.108153 · 2022 · External reference
Robust and distributionally robust optimization models for linear support vector machine
10.1016/j.cor.2022.105930 · 2022 · External reference
An improved grid search algorithm to optimize SVR for prediction
10.1007/s00500-020-05560-w · 2021 · External reference
State-of-charge estimation for battery management system using optimized support vector machine for regression
10.1016/j.jpowsour.2014.07.016 · 2014 · External reference
Unresolved reference
2024 · External reference
Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model
2015 · External reference
Degradation-informed closed-loop feature optimization and SHAP-enhanced machine learning for lithium-ion battery SOH estimation
10.1016/j.jpowsour.2026.240784 · 2026 · External reference
Evolving safety challenges of power batteries in service: insights and strategies
10.1016/j.rser.2025.116621 · 2026 · External reference
Roll-to-roll scalable manufacturing of nanoporous separators for high-safety lithium-ion batteries
10.1021/acsnano.6c00772 · 2026 · External reference
Safety assessment of large-format quasi-solid-state lithium-ion batteries
10.1002/aenm.202504346 · 2025 · External reference
Enhancing crashworthiness performance of a battery pack system using multicell square tube structures
10.1016/j.euromechsol.2025.105665 · 2025 · External reference
Pressure-induced capacity recovery and performance enhancements in LTO/NMC-LCO batteries
10.1002/adfm.202419229 · ExternalCitation · doi-reference
Capacity estimation and knee point prediction using electrochemical impedance spectroscopy for lithium metal battery degradation via machine learning
10.1002/advs.202502336 · ExternalCitation · doi-reference
Safety assessment of large-format quasi-solid-state lithium-ion batteries
10.1002/aenm.202504346 · ExternalCitation · doi-reference
An improved grid search algorithm to optimize SVR for prediction
10.1007/s00500-020-05560-w · ExternalCitation · doi-reference
Development of an empirical aging model for Li-ion batteries and application to assess the impact of vehicle-to-grid strategies on battery lifetime
10.1016/j.apenergy.2016.03.119 · ExternalCitation · doi-reference
Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation
10.1016/j.apenergy.2022.120204 · ExternalCitation · doi-reference
Early prediction of battery lifetime based on graphical features and convolutional neural networks
10.1016/j.apenergy.2023.122048 · ExternalCitation · doi-reference
State-of-health estimation and knee point identification of lithium-ion battery based on data-driven and mechanism model
10.1016/j.apenergy.2025.125539 · ExternalCitation · doi-reference
Lithium-ion battery end of life prediction based on the decelerating aging point
10.1016/j.apenergy.2025.126692 · ExternalCitation · doi-reference
Multiaxial failure characterization and short-circuit prediction of large-format prismatic lithium-ion batteries
10.1016/j.apenergy.2025.126963 · ExternalCitation · doi-reference
Battery health prediction under data scarcity: a cross-domain physics-informed 5-shot framework with GRU-transformer
10.1016/j.apenergy.2025.127012 · ExternalCitation · doi-reference
SOH estimation framework for batteriesconsidering label normalization and feature stability under real-world data
10.1016/j.apenergy.2025.127023 · ExternalCitation · doi-reference
Multi-stress accelerated aging for cycle life evaluation of high-capacity, long-life Lithium Iron phosphate batteries
10.1016/j.apenergy.2025.127126 · ExternalCitation · doi-reference
Robust and distributionally robust optimization models for linear support vector machine
10.1016/j.cor.2022.105930 · ExternalCitation · doi-reference
Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells
10.1016/j.egyai.2020.100006 · ExternalCitation · doi-reference
A physics-based aging model for lithium-ion battery with coupled chemical/mechanical degradation mechanisms
10.1016/j.electacta.2021.139133 · ExternalCitation · doi-reference
Toward the performance evolution of lithium-ion battery upon impact loading
10.1016/j.electacta.2022.141192 · ExternalCitation · doi-reference
Lithium-ion batteries remaining useful life prediction based on BLS-RVM
10.1016/j.energy.2021.121269 · ExternalCitation · doi-reference
Electrochemical-mechanical understanding of the accelerated degradation of lithium-ion batteries caused by mechanical stress
10.1016/j.energy.2025.137420 · ExternalCitation · doi-reference
Rate-dependent damage and failure behavior of lithium-ion battery electrodes
10.1016/j.engfracmech.2024.110143 · ExternalCitation · doi-reference
Effect of external pressure and internal stress on battery performance and lifespan
10.1016/j.ensm.2022.07.034 · ExternalCitation · doi-reference
A new classification scheme based on extended Kalman filter and support vector machine
10.1016/j.epsr.2022.108153 · ExternalCitation · doi-reference
Early-stage lifetime prediction for lithium-ion batteries: a deep learning framework jointly considering machine-learned and handcrafted data features
10.1016/j.est.2022.104936 · ExternalCitation · doi-reference
Comprehensive investigation on the durability and safety performances of lithium-ion batteries under slight mechanical deformation
10.1016/j.est.2023.107450 · ExternalCitation · doi-reference
An electrochemical-thermal coupling model for lithium-ion battery state-of-charge estimation with improve dual particle filter framework
10.1016/j.est.2024.111473 · ExternalCitation · doi-reference
Progressive degradation behavior and mechanism of lithium-ion batteries subjected to minor deformation damage
10.1016/j.est.2024.113992 · ExternalCitation · doi-reference
Aging trajectory prediction of lithium-ion batteries based on mechanical-electrical features via nonlinear autoregressive and regression neural networks
10.1016/j.est.2024.114696 · ExternalCitation · doi-reference
Regression random machines: an ensemble support vector regression model with free kernel choice
10.1016/j.eswa.2022.117107 · ExternalCitation · doi-reference
Enhancing mechanical reliability and safety performance of a battery pack system for electric vehicles: a review
10.1016/j.etran.2025.100469 · ExternalCitation · doi-reference
Enhancing crashworthiness performance of a battery pack system using multicell square tube structures
10.1016/j.euromechsol.2025.105665 · ExternalCitation · doi-reference
Early-cycle prediction of battery aging onset across chemistries
10.1016/j.jechem.2025.01.035 · ExternalCitation · doi-reference
Systematic feature design for cycle life prediction of lithium-ion batteries during formation
10.1016/j.joule.2025.101884 · ExternalCitation · doi-reference
State-of-charge estimation for battery management system using optimized support vector machine for regression
10.1016/j.jpowsour.2014.07.016 · ExternalCitation · doi-reference
Lithium battery aging model based on Dakin’s degradation approach
10.1016/j.jpowsour.2016.06.036 · ExternalCitation · doi-reference
Gaussian process regression for forecasting battery state of health
10.1016/j.jpowsour.2017.05.004 · ExternalCitation · doi-reference
High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with gaussian process regression
10.1016/j.jpowsour.2023.232737 · ExternalCitation · doi-reference
Degradation-informed closed-loop feature optimization and SHAP-enhanced machine learning for lithium-ion battery SOH estimation
10.1016/j.jpowsour.2026.240784 · ExternalCitation · doi-reference
Remaining useful life prediction of Lithium-ion batteries based on wiener process under time-varying temperature condition
10.1016/j.ress.2021.107675 · ExternalCitation · doi-reference
Evolving safety challenges of power batteries in service: insights and strategies
10.1016/j.rser.2025.116621 · ExternalCitation · doi-reference
Roll-to-roll scalable manufacturing of nanoporous separators for high-safety lithium-ion batteries
10.1021/acsnano.6c00772 · ExternalCitation · doi-reference
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
10.1038/s41467-024-48779-z · ExternalCitation · doi-reference
Data-driven prediction of battery cycle life before capacity degradation
10.1038/s41560-019-0356-8 · ExternalCitation · doi-reference
Addressing the safety of next-generation batteries
10.1038/s41586-025-09358-4 · ExternalCitation · doi-reference
Machine learning pipeline for battery state-of-health estimation
10.1038/s42256-021-00312-3 · ExternalCitation · doi-reference
Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning
10.1038/s42256-024-00972-x · ExternalCitation · doi-reference
Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
10.1039/d2ee03019e · ExternalCitation · doi-reference
Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning
10.1039/d4ee03839h · ExternalCitation · doi-reference
Impact of external short circuit on lithium-ion batteries: a post-mortem case study
10.1039/d5ta00705d · ExternalCitation · doi-reference
Remaining useful life prediction of Lithium-ion batteries based on health Indicator and gaussian process regression model
10.1109/access.2019.2905740 · ExternalCitation · doi-reference
Intelligent data analysis using optimized support vector machine based data mining approach for tourism industry
10.1145/3494566 · ExternalCitation · doi-reference
