Research graph
References from Physics-informed entropy temporal neural network for cross-condition state-of-health estimation of lithium-ion batteries. Local targets link to admitted publications; unresolved targets remain external evidence.
A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges
10.1016/j.jechem.2024.06.017 · 2024 · External reference
Physics-based battery SOC estimation methods: Recent advances and future perspectives
10.1016/j.jechem.2023.09.045 · 2024 · External reference
Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
10.1039/d2ee03019e · 2023 · External reference
Degradation path approximation for remaining useful life estimation
10.1016/j.aei.2024.102422 · 2024 · External reference
Early prediction of battery lifetime based on graphical features and convolutional neural networks
10.1016/j.apenergy.2023.122048 · 2024 · External reference
A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations
10.1016/j.jclepro.2018.09.065 · 2018 · External reference
An improved coulomb counting method based on dual open-circuit voltage and real-time evaluation of battery dischargeable capacity considering temperature and battery aging
10.1002/er.7042 · 2021 · External reference
State of health estimation of lithium-ion battery using dual adaptive unscented Kalman filter and Coulomb counting approach
10.1016/j.est.2024.111557 · 2024 · External reference
Study of SOC estimation by the ampere-hour integral method with capacity correction based on LSTM
10.3390/batteries8100170 · 2022 · 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
State of health estimation for lithium-ion batteries with deep learning approach and direct current internal resistance
10.3390/en17112487 · 2024 · External reference
Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries
10.1016/j.ensm.2022.05.047 · 2022 · External reference
State of health estimation for lithium-ion batteries based on transition frequency’s impedance and other impedance features with correlation analysis
10.3390/batteries11040133 · 2025 · External reference
Joint estimation of lithium battery SOC-SOH based on ASRCKF algorithm
10.3390/pr13113620 · 2025 · External reference
State estimation and aging mechanism of 2nd life lithium-ion batteries: Non-destructive and postmortem combined analysis
10.1016/j.electacta.2023.141996 · 2023 · External reference
Predicting the state of charge and health of batteries using data-driven machine learning
10.1038/s42256-020-0156-7 · 2020 · External reference
Machine learning in state of health and remaining useful life estimation: theoretical and technological development in battery degradation modelling
10.1016/j.rser.2021.111903 · 2022 · External reference
Critical summary and perspectives on state-of-health of lithium-ion battery
10.1016/j.rser.2023.114077 · 2024 · External reference
Lithium-ion battery SOC/SOH adaptive estimation via simplified single particle model
10.1002/er.5374 · 2020 · External reference
State of health estimation of lithium-ion batteries based on equivalent circuit model and data-driven method
10.1016/j.est.2023.109195 · 2023 · External reference
Bayesian analysis of interpretable aging across thousands of commercial lithium-ion batteries
2025 · External reference
State of charge and state of health estimation of lithium-ion battery packs with inconsistent internal parameters using dual extended Kalman filter
2024 · External reference
Model construction and dominant mechanism analysis of Li-ion batteries under periodic excitation
2024 · External reference
Data-driven prediction of battery cycle life before capacity degradation
10.1038/s41560-019-0356-8 · 2019 · External reference
Closed-loop optimization of fast-charging protocols for batteries with machine learning
10.1038/s41586-020-1994-5 · 2020 · External reference
Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods
10.1016/j.jpowsour.2012.11.146 · 2013 · External reference
Gaussian process regression for forecasting battery state of health
10.1016/j.jpowsour.2017.05.004 · 2017 · External reference
An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2
2024 · External reference
Application domain extension of incremental capacity-based battery SOH indicators
10.1016/j.energy.2021.121224 · 2021 · External reference
Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning
10.1039/d2ee01676a · 2022 · External reference
State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method
10.1016/j.est.2023.107161 · 2023 · External reference
Integrated framework for battery SOH estimation using incremental capacity and image feature transformation
10.1016/j.geits.2025.100366 · 2026 · External reference
SOH prediction for lithium-ion batteries by using historical state and future load information with an AM-seq2seq model
10.1016/j.apenergy.2023.120793 · 2023 · External reference
A unified deep learning optimization paradigm for lithium-ion battery state-of-health estimation
10.1109/tec.2023.3294540 · 2024 · External reference
Integrated framework for SOH estimation of lithium-ion batteries under capacity heterogeneity in real-world fleets
10.1016/j.geits.2026.100440 · 2026 · External reference
Explainability-driven model improvement for SOH estimation of lithium-ion battery
10.1016/j.ress.2022.109046 · 2023 · External reference
A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries
10.1016/j.jechem.2021.12.004 · 2022 · External reference
Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction
10.1016/j.ymssp.2022.109347 · 2022 · External reference
Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods
2024 · External reference
Physics-informed learning of governing equations from scarce data
10.1038/s41467-021-26434-1 · 2021 · External reference
Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features
10.1016/j.energy.2021.121712 · 2022 · External reference
A hybrid battery equivalent circuit model, deep learning, and transfer learning for battery state monitoring
10.1109/tte.2022.3204843 · 2023 · External reference
Universal differential equations for battery modelling
10.1016/j.apenergy.2024.123692 · 2024 · External reference
A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks
10.1038/s41598-023-33018-0 · 2023 · External reference
Bayesian model averaging for uncertainty quantification in lithium-ion battery prognosis
2024 · External reference
State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles
10.1016/j.energy.2022.126064 · 2023 · External reference
An improved generic hybrid prognostic method for RUL prediction based on PF-LSTM learning
10.1109/tim.2023.3251391 · 2023 · External reference
A physics-constrained Bayesian neural network for battery remaining useful life prediction
10.1016/j.apm.2023.05.038 · 2023 · External reference
Physics-informed machine learning
10.1038/s42254-021-00314-5 · 2021 · External reference
Physics-informed neural networks for state of health estimation in lithium-ion batteries
10.1149/1945-7111/acf0ef · 2023 · External reference
Inherently interpretable physics-informed neural network for battery modeling and prognosis
2023 · External reference
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · 2019 · External reference
Deep hidden physics models: Deep learning of nonlinear partial differential equations
2018 · External reference
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
10.1038/s41467-024-48779-z · 2024 · External reference
Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy
10.1016/j.aei.2024.102972 · 2025 · External reference
Cumulative spectrum distribution entropy for rotating machinery fault diagnosis
10.1016/j.ymssp.2023.110905 · 2024 · External reference
A generalizable physics-informed neural network for lithium-ion battery SOH estimation utilizing partial charging segments
10.1016/j.jechem.2025.08.093 · 2026 · External reference
CHAIN: Cyber hierarchy and interactional network enabling digital solution for battery full-lifespan management
10.1016/j.matt.2020.04.015 · 2020 · External reference
Bridging multiscale characterization technologies and digital modeling to evaluate lithium battery full lifecycle
2022 · External reference
A model for predicting capacity fade due to SEI formation in a commercial graphite/LiFePO4 cell
10.1149/2.0641506jes · 2015 · External reference
Lithium-ion battery SOC/SOH adaptive estimation via simplified single particle model
10.1002/er.5374 · ExternalCitation · doi-reference
An improved coulomb counting method based on dual open-circuit voltage and real-time evaluation of battery dischargeable capacity considering temperature and battery aging
10.1002/er.7042 · ExternalCitation · doi-reference
Degradation path approximation for remaining useful life estimation
10.1016/j.aei.2024.102422 · ExternalCitation · doi-reference
Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy
10.1016/j.aei.2024.102972 · ExternalCitation · doi-reference
SOH prediction for lithium-ion batteries by using historical state and future load information with an AM-seq2seq model
10.1016/j.apenergy.2023.120793 · 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
Universal differential equations for battery modelling
10.1016/j.apenergy.2024.123692 · ExternalCitation · doi-reference
A physics-constrained Bayesian neural network for battery remaining useful life prediction
10.1016/j.apm.2023.05.038 · ExternalCitation · doi-reference
State estimation and aging mechanism of 2nd life lithium-ion batteries: Non-destructive and postmortem combined analysis
10.1016/j.electacta.2023.141996 · ExternalCitation · doi-reference
Application domain extension of incremental capacity-based battery SOH indicators
10.1016/j.energy.2021.121224 · ExternalCitation · doi-reference
Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features
10.1016/j.energy.2021.121712 · ExternalCitation · doi-reference
State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles
10.1016/j.energy.2022.126064 · ExternalCitation · doi-reference
Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries
10.1016/j.ensm.2022.05.047 · ExternalCitation · doi-reference
State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method
10.1016/j.est.2023.107161 · ExternalCitation · doi-reference
State of health estimation of lithium-ion batteries based on equivalent circuit model and data-driven method
10.1016/j.est.2023.109195 · ExternalCitation · doi-reference
State of health estimation of lithium-ion battery using dual adaptive unscented Kalman filter and Coulomb counting approach
10.1016/j.est.2024.111557 · ExternalCitation · doi-reference
Integrated framework for battery SOH estimation using incremental capacity and image feature transformation
10.1016/j.geits.2025.100366 · ExternalCitation · doi-reference
Integrated framework for SOH estimation of lithium-ion batteries under capacity heterogeneity in real-world fleets
10.1016/j.geits.2026.100440 · ExternalCitation · doi-reference
A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations
10.1016/j.jclepro.2018.09.065 · ExternalCitation · doi-reference
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · ExternalCitation · doi-reference
A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries
10.1016/j.jechem.2021.12.004 · ExternalCitation · doi-reference
Physics-based battery SOC estimation methods: Recent advances and future perspectives
10.1016/j.jechem.2023.09.045 · ExternalCitation · doi-reference
A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges
10.1016/j.jechem.2024.06.017 · ExternalCitation · doi-reference
A generalizable physics-informed neural network for lithium-ion battery SOH estimation utilizing partial charging segments
10.1016/j.jechem.2025.08.093 · ExternalCitation · doi-reference
Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods
10.1016/j.jpowsour.2012.11.146 · 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
CHAIN: Cyber hierarchy and interactional network enabling digital solution for battery full-lifespan management
10.1016/j.matt.2020.04.015 · ExternalCitation · doi-reference
Explainability-driven model improvement for SOH estimation of lithium-ion battery
10.1016/j.ress.2022.109046 · ExternalCitation · doi-reference
Machine learning in state of health and remaining useful life estimation: theoretical and technological development in battery degradation modelling
10.1016/j.rser.2021.111903 · ExternalCitation · doi-reference
Critical summary and perspectives on state-of-health of lithium-ion battery
10.1016/j.rser.2023.114077 · ExternalCitation · doi-reference
Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction
10.1016/j.ymssp.2022.109347 · ExternalCitation · doi-reference
Cumulative spectrum distribution entropy for rotating machinery fault diagnosis
10.1016/j.ymssp.2023.110905 · ExternalCitation · doi-reference
Physics-informed learning of governing equations from scarce data
10.1038/s41467-021-26434-1 · 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
Closed-loop optimization of fast-charging protocols for batteries with machine learning
10.1038/s41586-020-1994-5 · ExternalCitation · doi-reference
A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks
10.1038/s41598-023-33018-0 · ExternalCitation · doi-reference
Physics-informed machine learning
10.1038/s42254-021-00314-5 · ExternalCitation · doi-reference
Predicting the state of charge and health of batteries using data-driven machine learning
10.1038/s42256-020-0156-7 · ExternalCitation · doi-reference
Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning
10.1039/d2ee01676a · ExternalCitation · doi-reference
Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
10.1039/d2ee03019e · ExternalCitation · doi-reference
A unified deep learning optimization paradigm for lithium-ion battery state-of-health estimation
10.1109/tec.2023.3294540 · ExternalCitation · doi-reference
An improved generic hybrid prognostic method for RUL prediction based on PF-LSTM learning
10.1109/tim.2023.3251391 · ExternalCitation · doi-reference
A hybrid battery equivalent circuit model, deep learning, and transfer learning for battery state monitoring
10.1109/tte.2022.3204843 · ExternalCitation · doi-reference
Physics-informed neural networks for state of health estimation in lithium-ion batteries
10.1149/1945-7111/acf0ef · ExternalCitation · doi-reference
A model for predicting capacity fade due to SEI formation in a commercial graphite/LiFePO4 cell
10.1149/2.0641506jes · ExternalCitation · doi-reference
State of health estimation for lithium-ion batteries based on transition frequency’s impedance and other impedance features with correlation analysis
10.3390/batteries11040133 · ExternalCitation · doi-reference
Study of SOC estimation by the ampere-hour integral method with capacity correction based on LSTM
10.3390/batteries8100170 · ExternalCitation · doi-reference
State of health estimation for lithium-ion batteries with deep learning approach and direct current internal resistance
10.3390/en17112487 · ExternalCitation · doi-reference
Joint estimation of lithium battery SOC-SOH based on ASRCKF algorithm
10.3390/pr13113620 · ExternalCitation · doi-reference
