Research graph
References from Towards interpretable and trustworthy lithium-ion battery prognostics and health management with physics-informed machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2021 · External reference
A review of lithium-ion battery safety concerns: The issues, strategies, and testing standards
10.1016/j.jechem.2020.10.017 · 2021 · External reference
A reflection on lithium-ion battery cathode chemistry
10.1038/s41467-020-15355-0 · 2020 · External reference
Unresolved reference
2025 · External reference
Unresolved reference
2024 · External reference
Lithium-ion battery sudden death: Safety degradation and failure mechanism
10.1016/j.etran.2024.100333 · 2024 · External reference
Battery safety: Fault diagnosis from laboratory to real world
10.1016/j.jpowsour.2024.234111 · 2024 · External reference
Accurate battery models matter: Improving battery performance assessment using a novel energy management architecture
10.1016/j.jpowsour.2025.236216 · 2025 · 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
A review of the state of health for lithium-ion batteries: Research status and suggestions
10.1016/j.jclepro.2020.120813 · 2020 · External reference
Unresolved reference
2025 · External reference
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
10.1038/s44296-024-00011-1 · 2024 · External reference
A review on prognostics and health management (PHM) methods of lithium-ion batteries
10.1016/j.rser.2019.109405 · 2019 · External reference
Unresolved reference
2021 · External reference
Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning
10.1038/s42256-024-00972-x · 2025 · External reference
A general framework for lithium-ion battery state of health estimation: From laboratory tests to machine learning with transferability across domains
10.1016/j.apenergy.2024.125086 · 2025 · External reference
Synergizing physics and machine learning for advanced battery management
10.1038/s44172-024-00273-6 · 2024 · 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
Review of “grey box” lifetime modeling for lithium-ion battery: Combining physics and data-driven methods
10.1016/j.est.2022.105992 · 2022 · External reference
Recent advances in artificial intelligence-driven prognostics and health management of mobility batteries
10.1007/s42493-025-00126-0 · 2025 · External reference
Analyzing electric vehicle battery health performance using supervised machine learning
10.1016/j.rser.2023.113967 · 2024 · External reference
Comprehensive review of machine learning, deep learning, and digital twin data-driven approaches in battery health prediction of electric vehicles
10.1109/access.2024.3380452 · 2024 · External reference
Evaluation of advances in battery health prediction for electric vehicles from traditional linear filters to latest machine learning approaches
10.3390/batteries10100356 · 2024 · External reference
Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review
10.1002/eom2.12345 · 2023 · External reference
A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges
10.1016/j.rser.2025.116125 · 2025 · External reference
Interpretable machine learning for battery prognosis: Retrospect and prospect
10.1002/aenm.202503067 · 2025 · External reference
A review of non-probabilistic machine learning-based state of health estimation techniques for lithium-ion battery
10.1016/j.apenergy.2021.117346 · 2021 · External reference
Machine learning for battery systems applications: Progress, challenges, and opportunities
10.1016/j.jpowsour.2024.234272 · 2024 · External reference
Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review
10.1016/j.rser.2023.114224 · 2024 · External reference
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2026 · External reference
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Battery health diagnostics: Bridging the gap between academia and industry
10.1016/j.etran.2023.100309 · 2024 · External reference
State estimation of lithium-ion batteries via physics-machine learning combined methods: A methodological review and future perspectives
10.1016/j.etran.2025.100420 · 2025 · External reference
Li-ion battery materials: present and future
10.1016/j.mattod.2014.10.040 · 2015 · External reference
Unresolved reference
2023 · External reference
A comparison between physics-based Li-ion battery models
10.1016/j.electacta.2024.144360 · 2024 · External reference
Review of simplified Pseudo-two-Dimensional models of lithium-ion batteries
10.1016/j.jpowsour.2016.07.036 · 2016 · External reference
State-of-the-art lithium-ion battery recycling technologies
2022 · External reference
Modeling and validation for performance analysis and impedance spectroscopy characterization of lithium-ion batteries
10.1016/j.nxener.2024.100153 · 2024 · External reference
Degradation diagnostics for lithium ion cells
10.1016/j.jpowsour.2016.12.011 · 2017 · External reference
A review on the key issues of the lithium ion battery degradation among the whole life cycle
10.1016/j.etran.2019.100005 · 2019 · External reference
Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell
10.1149/1.2221597 · 1993 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
2024 · External reference
Theory of SEI formation in rechargeable batteries: Capacity fade, accelerated aging and lifetime prediction
10.1149/2.044302jes · 2012 · External reference
Mechanistic modeling of li plating in lithium-ion batteries
10.1016/j.jpowsour.2021.230936 · 2022 · External reference
Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries
10.1149/2.0281914jes · 2019 · External reference
A SEI modeling approach distinguishing between capacity and power fade
10.1149/2.0321712jes · 2017 · External reference
Generation and evolution of the solid electrolyte interphase of lithium-ion batteries
10.1016/j.joule.2019.08.018 · 2019 · External reference
Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries
10.1038/s41524-018-0064-0 · 2018 · External reference
Ageing mechanisms in lithium-ion batteries
10.1016/j.jpowsour.2005.01.006 · 2005 · External reference
Electrode heterogeneous modeling and cross-scale analysis under multi-physics coupling: Microstructure-dependent mechanism for nonlinear degradation
2025 · External reference
A comparison of standard SEI growth models in the context of battery formation
10.1149/1945-7111/ad8548 · 2024 · External reference
Development of first principles capacity fade model for Li-ion cells
10.1149/1.1634273 · 2004 · External reference
Identifying the mechanism of continued growth of the solid–electrolyte interphase
10.1002/cssc.201800077 · 2018 · External reference
Solvent diffusion model for aging of lithium-ion battery cells
10.1149/1.1644601 · 2004 · External reference
Mathematical modeling of the lithium deposition overcharge reaction in lithium-ion batteries using carbon-based negative electrodes
10.1149/1.1392512 · 1999 · External reference
Research progress of lithium plating on graphite anode in lithium-ion batteries
10.1002/cjoc.202000512 · 2021 · External reference
High lithium metal cycling efficiency in a room-temperature ionic liquid
10.1149/1.1664051 · 2004 · External reference
Modeling of lithium plating and lithium stripping in lithium-ion batteries
10.1016/j.jpowsour.2018.12.084 · 2019 · External reference
Onset of lithium plating in fast-charging Li-ion batteries
10.1021/acsenergylett.5c00322 · 2025 · External reference
Lithium-ion battery degradation: how to model it
10.1039/d2cp00417h · 2022 · External reference
Numerical simulation of intercalation-induced stress in Li-ion battery electrode particles
10.1149/1.2759840 · 2007 · External reference
Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine
10.1016/j.energy.2018.06.220 · 2018 · External reference
Physics-informed neural network for spacecraft lithium-ion battery modeling and health diagnosis
10.1109/tmech.2023.3348519 · 2024 · 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
State-of-health estimation for lithium-ion battery using model-based feature optimization and deep extreme learning machine
10.1016/j.est.2023.108732 · 2023 · External reference
A hybrid battery equivalent circuit model, deep learning, and transfer learning for battery state monitoring
10.1109/tte.2022.3204843 · 2022 · External reference
Calendar-and cycle-life studies of advanced technology development program generation 1 lithium-ion batteries
10.1016/s0378-7753(02)00210-0 · 2002 · External reference
Cycle-life model for graphite-LiFePO4 cells
10.1016/j.jpowsour.2010.11.134 · 2011 · External reference
Degradation behavior of lithium-ion batteries during calendar ageing—The case of the internal resistance increase
10.1109/tia.2017.2756026 · 2017 · External reference
Empirical calendar ageing model for electric vehicles and energy storage systems batteries
10.1016/j.est.2022.105676 · 2022 · External reference
Modeling of lithium-ion battery degradation for cell life assessment
10.1109/tsg.2016.2578950 · 2016 · External reference
A practical semi-empirical model for predicting the SoH of lithium-ion battery: A novel perspective on short-term rest
10.1016/j.est.2024.112659 · 2024 · External reference
Unresolved reference
2014 · External reference
Applicability of available Li-ion battery degradation models for system and control algorithm design
10.1016/j.conengprac.2017.10.002 · 2018 · External reference
Comprehensive performance comparison among different types of features in data-driven battery state of health estimation
10.1016/j.apenergy.2024.123555 · 2024 · External reference
Impedance characterization of lithium-ion batteries aging under high-temperature cycling: Importance of electrolyte-phase diffusion
10.1016/j.jpowsour.2019.04.040 · 2019 · External reference
Physics-informed machine learning
10.1038/s42254-021-00314-5 · 2021 · External reference
10.36001/phmconf.2021.v13i1.2998
10.36001/phmconf.2021.v13i1.2998 · External reference
Perspective—combining physics and machine learning to predict battery lifetime
10.1149/1945-7111/abec55 · 2021 · External reference
Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries
10.1016/j.energy.2018.03.023 · 2018 · External reference
Differential voltage analyses of high-power, lithium-ion cells: 1. Technique and application
10.1016/j.jpowsour.2004.07.021 · 2005 · External reference
State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
10.1016/j.isci.2021.103265 · 2021 · External reference
Non-invasive characteristic curve analysis of lithium-ion batteries enabling degradation analysis and data-driven model construction: A review
10.1007/s42154-022-00181-5 · 2022 · External reference
Investigation of lithium-ion battery degradation by corrected differential voltage analysis based on reference electrode
10.1016/j.apenergy.2025.125735 · 2025 · External reference
Synthesize battery degradation modes via a diagnostic and prognostic model
10.1016/j.jpowsour.2012.07.016 · 2012 · External reference
A machine learning framework for early detection of lithium plating combining multiple physics-based electrochemical signatures
2021 · External reference
Lithium-ion battery state of health estimation using simple regression model based on incremental capacity analysis features
10.3390/en16207066 · 2023 · External reference
Online state-of-health estimation of lithium-ion battery based on incremental capacity curve and BP neural network
10.3390/batteries8040029 · 2022 · External reference
Classification of aged batteries based on capacity and/or resistance through machine learning models with aging features as input: A comparative study
10.1016/j.jclepro.2024.143431 · 2024 · External reference
On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression
10.1016/j.jpowsour.2013.02.012 · 2013 · External reference
Data-driven ICA-Bi-LSTM-combined lithium battery SOH estimation
2022 · External reference
Lithium-ion battery capacity estimation based on incremental capacity analysis and deep convolutional neural network
10.3390/en17061272 · 2024 · External reference
A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve
10.1016/j.jpowsour.2018.03.015 · 2018 · External reference
State of health estimation for Li-ion battery using incremental capacity analysis and Gaussian process regression
10.1016/j.energy.2019.116467 · 2020 · External reference
Offline and online blended machine learning for lithium-ion battery health state estimation
10.1109/tte.2021.3129479 · 2021 · External reference
SOH prediction of lithium battery based on IC curve feature and BP neural network
10.1016/j.energy.2022.125234 · 2022 · External reference
ICFormer: A Deep Learning model for informed lithium-ion battery diagnosis and early knee detection
10.1016/j.jpowsour.2023.233910 · 2024 · External reference
Novel differential voltage features based machine learning approach to lithium-ion batteries SoH prediction at various C-rates
2025 · External reference
Remaining useful life early prediction of batteries based on the differential voltage and differential capacity curves
10.1109/tim.2021.3117631 · 2021 · External reference
Regeneration of lithium-ion battery impedance using a novel machine learning framework and minimal empirical data
10.1016/j.est.2022.105022 · 2022 · External reference
Transfer learning-based lithium-ion battery state of health estimation with electrochemical impedance spectroscopy
10.1109/tte.2025.3533540 · 2025 · External reference
Accelerated state of health estimation of second life lithium-ion batteries via electrochemical impedance spectroscopy tests and machine learning techniques
10.1016/j.est.2022.106295 · 2023 · External reference
Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model
10.1016/j.jpowsour.2022.232030 · 2022 · External reference
Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep learning model
10.1016/j.isci.2022.105638 · 2022 · External reference
A data-fusion framework for lithium battery health condition estimation based on differential thermal voltammetry
10.1016/j.energy.2021.122206 · 2022 · External reference
Feature engineering for machine learning enabled early prediction of battery lifetime
10.1016/j.jpowsour.2022.231127 · 2022 · External reference
Domain knowledge-guided machine learning framework for state of health estimation in lithium-ion batteries
10.1038/s44172-024-00304-2 · 2024 · External reference
Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features
10.1016/j.energy.2021.121712 · 2022 · External reference
A novel aging characteristics-based feature engineering for battery state of health estimation
10.1016/j.energy.2023.127169 · 2023 · External reference
Battery aging mode identification across NMC compositions and designs using machine learning
10.1016/j.joule.2022.10.016 · 2022 · External reference
Machine learning pipeline for battery state-of-health estimation
10.1038/s42256-021-00312-3 · 2021 · External reference
Data-driven prediction of battery cycle life before capacity degradation
10.1038/s41560-019-0356-8 · 2019 · External reference
Battery capacity fading estimation using a force-based incremental capacity analysis
10.1149/2.0511608jes · 2016 · External reference
Comparison of expansion and voltage differential indicators for battery capacity fade
10.1016/j.jpowsour.2021.230714 · 2022 · External reference
Deep learning to estimate lithium-ion battery state of health without additional degradation experiments
10.1038/s41467-023-38458-w · 2023 · External reference
Predictive battery health management with transfer learning and online model correction
10.1109/tvt.2021.3055811 · 2021 · External reference
Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction
10.1016/j.est.2022.104646 · 2022 · External reference
A transferable lithium-ion battery remaining useful life prediction method from cycle-consistency of degradation trend
10.1016/j.jpowsour.2022.230975 · 2022 · External reference
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
10.1038/s41467-024-48779-z · 2024 · External reference
A comparative study of battery state-of-health estimation based on empirical mode decomposition and neural network
10.1016/j.est.2022.105333 · 2022 · External reference
Machine learning based battery pack health prediction using real-world data
10.1016/j.energy.2024.132856 · 2024 · External reference
A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data
10.1016/j.jpowsour.2022.231110 · 2022 · External reference
Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery
10.1016/j.aei.2021.101405 · 2021 · External reference
State-of-health estimation for the lithium-ion battery based on gradient boosting decision tree with autonomous selection of excellent features
10.1002/er.7292 · 2022 · External reference
Multi-modal framework for battery state of health evaluation using open-source electric vehicle data
10.1038/s41467-025-56485-7 · 2025 · External reference
Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions
10.1016/j.energy.2023.128971 · 2023 · External reference
State of health estimation of the lithium-ion power battery based on the principal component analysis-particle swarm optimization-back propagation neural network
10.1016/j.energy.2023.129061 · 2023 · External reference
Battery state-of-health modelling by multiple linear regression
10.1016/j.jclepro.2020.125700 · 2021 · External reference
Sensitivity analysis for reliable state-of-health estimation based on battery partial charging
10.1016/j.xcrp.2025.102646 · 2025 · External reference
State-of-health identification of lithium-ion batteries based on nonlinear frequency response analysis: First steps with machine learning
10.3390/app8050821 · 2018 · External reference
Machine learning predictions of lithium-ion battery state-of-health for eVTOL applications
10.1016/j.jpowsour.2022.232051 · 2022 · External reference
State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree
10.1016/j.energy.2023.129460 · 2023 · External reference
Battery health prediction using fusion-based feature selection and machine learning
10.1109/tte.2020.3017090 · 2020 · External reference
Li-ion battery prognostic and health management through an indirect hybrid model
10.1016/j.est.2021.102990 · 2021 · External reference
Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network
10.1016/j.energy.2021.121022 · 2021 · External reference
Battery health prognosis in data-deficient practical scenarios via reconstructed voltage-based machine learning
2025 · External reference
Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications
10.1016/j.apenergy.2023.121747 · 2023 · External reference
State-of-health estimation of lithium-ion batteries using incremental capacity analysis based on voltage–capacity model
10.1109/tte.2020.2994543 · 2020 · External reference
State of health estimation of cycle aged large format lithium-ion cells based on partial charging
10.1016/j.est.2021.103855 · 2022 · External reference
A state-of-health estimation method based on incremental capacity analysis for Li-ion battery considering charging/discharging rate
10.1016/j.est.2023.109010 · 2023 · External reference
Temperature dependency of diagnostic methods in lithium-ion batteries
10.1016/j.est.2022.104721 · 2022 · External reference
Real-world battery diagnostics in Industry 4.0
2025 · External reference
Data science approaches for electrochemical engineers: An introduction through surrogate model development for lithium-ion batteries
10.1149/2.1391714jes · 2018 · External reference
Lithium-ion battery digitalization: Combining physics-based models and machine learning
10.1016/j.rser.2024.114577 · 2024 · External reference
Physics-informed machine learning model for battery state of health prognostics using partial charging segments
10.1016/j.ymssp.2022.109002 · 2022 · External reference
A method for estimating lithium-ion battery state of health based on physics-informed hybrid neural network
10.1016/j.electacta.2025.146110 · 2025 · External reference
Using partial discharge data to identify highly sensitive electrochemical parameters of aged lithium-ion batteries
2024 · External reference
Bayesian calibrated physics-informed neural networks for second-life battery SOH estimation
10.1016/j.ress.2025.111432 · 2025 · External reference
A hybrid temperature distribution monitoring method for lithium-ion battery module by integrating multi-physics with machine learning
10.1016/j.ijheatmasstransfer.2025.127278 · 2025 · External reference
A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries
2024 · External reference
Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics
10.1038/s41467-023-38895-7 · 2023 · External reference
Sensorless battery expansion estimation using electromechanical coupled models and machine learning
10.1016/j.jechem.2024.12.068 · 2025 · 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
Battery state-of-health diagnostics during fast cycling using physics-informed deep-learning
10.1016/j.jpowsour.2023.233582 · 2023 · External reference
A physics-informed hybrid data-driven approach with generative electrode-level features for lithium-ion battery health prognostics
2024 · External reference
Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries
10.1016/j.jpowsour.2021.230034 · 2021 · External reference
Advancing battery safety: Integrating multiphysics and machine learning for thermal runaway prediction in lithium-ion battery module
2024 · External reference
Machine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries’ thermal management
10.1016/j.ijheatmasstransfer.2021.121199 · 2021 · External reference
Extreme learning machine-based thermal model for lithium-ion batteries of electric vehicles under external short circuit
10.1016/j.eng.2020.08.015 · 2021 · External reference
Deep learning method for online parameter identification of lithium-ion batteries using electrochemical synthetic data
2024 · External reference
Learning physics based models of lithium-ion batteries
10.1016/j.ifacol.2021.08.225 · 2021 · External reference
Physics-informed neural networks for state of health estimation in lithium-ion batteries
10.1149/1945-7111/acf0ef · 2023 · External reference
Multiphysics-informed machine learning for battery design and health monitoring
2023 · External reference
Modeling capacity fade in lithium-ion cells
10.1016/j.jpowsour.2004.08.017 · 2005 · External reference
Machine learning-based state of health prediction for battery systems in real-world electric vehicles
10.1016/j.est.2023.107426 · 2023 · External reference
Physics-informed machine learning for accurate SOH estimation of lithium-ion batteries considering various temperatures and operating conditions
10.1016/j.energy.2025.134937 · 2025 · External reference
A physics-enhanced online joint estimation method for SOH and SOC of lithium-ion batteries in eVTOL aircraft applications
10.1016/j.est.2025.115567 · 2025 · External reference
In-situ battery life prognostics amid mixed operation conditions using physics-driven machine learning
10.1016/j.jpowsour.2023.233246 · 2023 · External reference
Managing battery performance degradation using physics-informed learning scheme for multiple health indicators
10.1109/tte.2025.3525742 · 2025 · External reference
State of health prognostics for series battery packs: A universal deep learning method
10.1016/j.energy.2021.121857 · 2022 · External reference
State of health estimation method for lithium-ion batteries based on multiple dynamic operating conditions
10.1016/j.jpowsour.2023.233541 · 2023 · External reference
Rapid health estimation of in-service battery packs based on limited labels and domain adaptation
10.1016/j.jechem.2023.10.056 · 2024 · External reference
Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
10.1039/d2ee03019e · 2023 · External reference
Determination of optimal indicators based on statistical analysis for the state of health estimation of a lithium-ion battery
10.3389/fenrg.2021.690266 · 2021 · External reference
A novel SOC-OCV separation and extraction technology suitable for online joint estimation of SOC and SOH in lithium-ion batteries
2025 · External reference
Fractional variable-order observer-based method for state-of-charge estimation of lithium-ion batteries
10.1016/j.apenergy.2025.125775 · 2025 · External reference
A review on lithium-ion battery ageing mechanisms and estimations for automotive applications
10.1016/j.jpowsour.2013.05.040 · 2013 · External reference
Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter
10.1016/j.energy.2024.130555 · 2024 · External reference
Empirical model, capacity recovery-identification correction and machine learning co-driven Li-ion battery remaining useful life prediction
10.1016/j.est.2024.114274 · 2024 · External reference
Remaining useful life prediction of lithium battery based on capacity regeneration point detection
10.1016/j.energy.2021.121233 · 2021 · External reference
Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries
10.1016/j.apenergy.2022.119011 · 2022 · External reference
Machine learning-based state-of-health estimation of battery management systems using experimental and simulation data
10.3390/math13142247 · 2025 · External reference
An empirical-informed model for the early degradation trajectory prediction of lithium-ion battery
10.1109/tec.2024.3385093 · 2024 · External reference
Accelerated battery life predictions through synergistic combination of physics-based models and machine learning
2022 · External reference
A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities
10.1016/j.ymssp.2024.111120 · 2024 · External reference
State of health estimation for lithium-ion battery using empirical degradation and error compensation models
10.1109/access.2020.3005229 · 2020 · 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
Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning
10.1016/j.jechem.2023.12.043 · 2024 · External reference
Physics-informed battery degradation prediction: Forecasting charging curves using one-cycle data
10.1016/j.jechem.2024.10.018 · 2025 · External reference
Exploiting domain knowledge to reduce data requirements for battery health monitoring
2024 · External reference
A battery state of health estimation method for real-world electric vehicles based on physics-informed neural networks
2025 · External reference
Battery intelligent temperature warning model with physically-informed attention residual networks
10.1016/j.apenergy.2025.125627 · 2025 · External reference
A physics-constrained Bayesian neural network for battery remaining useful life prediction
10.1016/j.apm.2023.05.038 · 2023 · External reference
Predicting lithium-ion battery health using attention mechanism with Kolmogorov-Arnold and physics-informed neural networks
2025 · External reference
A lightweight two-stage physics-informed neural network for SOH estimation of lithium-ion batteries with different chemistries
10.1016/j.jechem.2025.01.057 · 2025 · External reference
A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain
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Physics-informed neural networks for small sample state of health estimation of lithium-ion batteries
10.1016/j.est.2025.116559 · 2025 · External reference
Generative-enhanced physics-informed neural network for lithium-ion battery state of health estimation
2025 · External reference
Remaining useful life estimation for lithium-ion batteries using physics-informed neural networks
2024 · External reference
Enhanced electrode-level diagnostics for lithium-ion battery degradation using physics-informed neural networks
2025 · External reference
Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods
2024 · External reference
Forecasting battery capacity for second-life applications using physics-informed recurrent neural networks
10.1016/j.etran.2025.100432 · 2025 · External reference
A physics-informed composite network for modeling of electrochemical process of large-scale lithium-ion batteries
2024 · External reference
State of health estimation of lithium-ion battery cell based on optical thermometry with physics-informed machine learning
10.1016/j.engappai.2024.109704 · 2025 · External reference
Physics-informed machine learning estimation of the temperature of large-format lithium-ion batteries under various operating conditions
10.1016/j.applthermaleng.2025.126200 · 2025 · External reference
Modeling and prediction of lithium-ion battery thermal runaway via multiphysics-informed neural network
10.1016/j.est.2023.106654 · 2023 · External reference
An extended single-particle model based on physics-informed neural network for SOC state estimation of lithium-ion batteries
2024 · External reference
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