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Estimating the State of Health of Lithium-Ion Batteries Using Relaxation Voltage Data Based on the TCN-Transformer Model

https://doi.org/10.2139/ssrn.7182111
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inferredcites →https://doi.org/10.1016/j.apenergy.2018.01.011openalex-snapshot
A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation
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Random forest regression for online capacity estimation of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.asoc.2022.109615openalex-snapshot
Online state-of-health estimation of lithium-ion battery based on relevance vector machine with dynamic integration
inferredcites →https://doi.org/10.1016/j.electacta.2019.02.055openalex-snapshot
State-of-health (SOH) evaluation on lithium-ion battery by simulating the voltage relaxation curves
inferredcites →https://doi.org/10.1016/j.est.2022.104144openalex-snapshot
Early Quality Classification and Prediction of Battery Cycle Life in Production Using Machine Learning
inferredcites →https://doi.org/10.1016/j.est.2022.104512openalex-snapshot
State of health estimation based on modified Gaussian process regression for lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.est.2023.109195openalex-snapshot
State of health estimation of lithium-ion batteries based on equivalent circuit model and data-driven method
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The integration of the convolutional neural network and fourier neural network methods for the battery pack capacity prediction
inferredcites →https://doi.org/10.1016/j.geits.2023.100108openalex-snapshot
Battery SOH estimation method based on gradual decreasing current, double correlation analysis and GRU
inferredcites →https://doi.org/10.1016/j.jclepro.2018.09.065openalex-snapshot
A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations
inferredcites →https://doi.org/10.1016/j.joule.2021.09.015openalex-snapshot
Predicting the impact of formation protocols on battery lifetime immediately after manufacturing
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A comparative study of equivalent circuit models for Li-ion batteries
inferredcites →https://doi.org/10.1016/j.jpowsour.2013.03.158openalex-snapshot
Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications
inferredcites →https://doi.org/10.1016/j.jpowsour.2014.01.129openalex-snapshot
On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models
inferredcites →https://doi.org/10.1016/j.jpowsour.2017.11.094openalex-snapshot
Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles
inferredcites →https://doi.org/10.1016/j.jpowsour.2018.03.015openalex-snapshot
A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve
inferredcites →https://doi.org/10.1016/j.jpowsour.2022.231027openalex-snapshot
Battery health estimation with degradation pattern recognition and transfer learning
inferredcites →https://doi.org/10.1016/j.jpowsour.2022.232466openalex-snapshot
State of health estimation of lithium-ion battery with automatic feature extraction and self-attention learning mechanism
inferredcites →https://doi.org/10.1016/j.measurement.2022.111795openalex-snapshot
An improvement of equivalent circuit model for state of health estimation of lithium-ion batteries based on mid-frequency and low-frequency electrochemical impedance spectroscopy
inferredcites →https://doi.org/10.1016/j.rser.2015.11.042openalex-snapshot
Critical review of state of health estimation methods of Li-ion batteries for real applications
inferredcites →https://doi.org/10.1038/s41560-019-0356-8openalex-snapshot
Data-driven prediction of battery cycle life before capacity degradation
inferredcites →https://doi.org/10.1093/nsr/nwab146openalex-snapshot
Structural and chemical evolution in layered oxide cathodes of lithium-ion batteries revealed by synchrotron techniques
inferredcites →https://doi.org/10.1109/access.2020.2974401openalex-snapshot
Remaining Useful Life Prediction and State of Health Diagnosis of Lithium-Ion Battery Based on Second-Order Central Difference Particle Filter
inferredcites →https://doi.org/10.1109/tie.2019.2946551openalex-snapshot
Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries
inferredcites →https://doi.org/10.1109/tim.2008.2005965openalex-snapshot
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework
inferredcites →https://doi.org/10.1109/tnnls.2024.3411166openalex-snapshot
Residual Temporal Convolutional Network With Dual Attention Mechanism for Multilead-Time Interpretable Runoff Forecasting
inferredcites →https://doi.org/10.1109/tte.2017.2776558openalex-snapshot
State of Health Estimation of Lithium-Ion Batteries Using Capacity Fade and Internal Resistance Growth Models
inferredcites →https://doi.org/10.1109/tvt.2018.2796723openalex-snapshot
Model-Based Lithium-Ion Battery Resistance Estimation From Electric Vehicle Operating Data
inferredcites →https://doi.org/10.1109/tvt.2021.3055811openalex-snapshot
Predictive Battery Health Management With Transfer Learning and Online Model Correction
inferredcites →https://doi.org/10.1149/1.2221597openalex-snapshot
Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell
inferredcites →https://doi.org/10.3390/batteries8080077openalex-snapshot
Characteristics of Open Circuit Voltage Relaxation in Lithium-Ion Batteries for the Purpose of State of Charge and State of Health Analysis
inferredcites →https://doi.org/10.3390/en4040582openalex-snapshot
Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach
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