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An overview of data-driven battery health estimation technology for battery management system

NEUROCOMPUTING (2023)

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Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications

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Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries

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Battery state of health modeling and remaining useful life prediction through time series model

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Proceedings Paper Automation & Control Systems

State of health estimation for lithium-ion batteries with dynamic time warping and deep kernel learning model

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Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model

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State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis

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State of Health Monitoring and Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Temporal Convolutional Network

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State of Health Estimation for Lithium-Ion Battery Using Empirical Degradation and Error Compensation Models

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Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis

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Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression

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State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis

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Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries

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Lithium-Ion Battery State of health Monitoring Based on Ensemble Learning

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State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network

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State-of-Health Estimation for Lithium-Ion Batteries Based on Wiener Process With Modeling the Relaxation Effect

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A Method for Interval Prediction of Satellite Battery State of Health Based on Sample Entropy

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

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State-of-Health Prediction For Lithium-Ion Batteries With Multiple Gaussian Process Regression Model

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Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis

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Battery Health Prognosis Using Brownian Motion Modeling and Particle Filtering

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Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression

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State-of-Health Estimation for Lithium-Ion Batteries Based on the Multi-Island Genetic Algorithm and the Gaussian Process Regression

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State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking

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On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis

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State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application

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