state of health Latest Research Papers

Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2021.111903 ◽

2022 ◽

Vol 156 ◽

pp. 111903

Author(s):

Huzaifa Rauf ◽

Muhammad Khalid ◽

Naveed Arshad

Keyword(s):

Machine Learning ◽

Technological Development ◽

Remaining Useful Life ◽

State Of Health ◽

Life Estimation ◽

Battery Degradation ◽

Useful Life

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Principle component analysis-based optimized feature extraction merged with nonlinear regression model for improved state-of-health prediction

Journal of Energy Storage ◽

10.1016/j.est.2022.104026 ◽

2022 ◽

Vol 48 ◽

pp. 104026

Author(s):

Pyeong-Yeon Lee ◽

Sanguk Kwon ◽

Deokhun Kang ◽

Inho Cho ◽

Jonghoon Kim

Keyword(s):

Feature Extraction ◽

Regression Model ◽

Nonlinear Regression ◽

Principle Component Analysis ◽

Component Analysis ◽

Nonlinear Regression Model ◽

State Of Health ◽

Principle Component

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An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2021.111843 ◽

2022 ◽

Vol 156 ◽

pp. 111843

Author(s):

Penghua Li ◽

Zijian Zhang ◽

Radu Grosu ◽

Zhongwei Deng ◽

Jie Hou ◽

...

Keyword(s):

Neural Network ◽

Electric Vehicle ◽

Lithium Batteries ◽

Life Prediction ◽

Remaining Useful Life ◽

State Of Health ◽

Useful Life ◽

End To End

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Virtual experiments for battery state of health estimation based on neural networks and in-vehicle data

Journal of Energy Storage ◽

10.1016/j.est.2021.103856 ◽

2022 ◽

Vol 48 ◽

pp. 103856

Author(s):

Felix Heinrich ◽

Marco Pruckner

Keyword(s):

Neural Networks ◽

State Of Health ◽

Virtual Experiments ◽

Vehicle Data

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An estimation model for state of health of lithium-ion batteries using energy-based features

Journal of Energy Storage ◽

10.1016/j.est.2021.103846 ◽

2022 ◽

Vol 46 ◽

pp. 103846

Author(s):

Li Cai ◽

Jingdong Lin ◽

Xiaoyong Liao

Keyword(s):

Lithium Ion Batteries ◽

Lithium Ion ◽

State Of Health ◽

Estimation Model

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Improved State-of-health prediction based on auto-regressive integrated moving average with exogenous variables model in overcoming battery degradation-dependent internal parameter variation

Journal of Energy Storage ◽

10.1016/j.est.2021.103888 ◽

2022 ◽

Vol 46 ◽

pp. 103888

Author(s):

Seongwoo Kim ◽

Pyeong-Yeon Lee ◽

Miyoung Lee ◽

Jonghoon Kim ◽

Woonki Na

Keyword(s):

Moving Average ◽

Parameter Variation ◽

Internal Parameter ◽

State Of Health ◽

Exogenous Variables ◽

Battery Degradation ◽

Auto Regressive

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An encoder-decoder model based on deep learning for state of health estimation of lithium-ion battery

Journal of Energy Storage ◽

10.1016/j.est.2021.103804 ◽

2022 ◽

Vol 46 ◽

pp. 103804

Author(s):

Qingrui Gong ◽

Ping Wang ◽

Ze Cheng

Keyword(s):

Deep Learning ◽

Lithium Ion Battery ◽

Lithium Ion ◽

State Of Health ◽

Model Based

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State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network

Journal of Power Sources ◽

10.1016/j.jpowsour.2021.230892 ◽

2022 ◽

Vol 521 ◽

pp. 230892

Author(s):

Zheng Chen ◽

Hongqian Zhao ◽

Yuanjian Zhang ◽

Shiquan Shen ◽

Jiangwei Shen ◽

...

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Lithium Ion ◽

State Of Health ◽

Temperature Prediction ◽

Gated Recurrent Unit

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State of health estimation of cycle aged large format lithium-ion cells based on partial charging

Journal of Energy Storage ◽

10.1016/j.est.2021.103855 ◽

2022 ◽

Vol 46 ◽

pp. 103855

Author(s):

Samppa Jenu ◽

Ari Hentunen ◽

Jari Haavisto ◽

Mikko Pihlatie

Keyword(s):

Lithium Ion ◽

State Of Health ◽

Large Format ◽

Lithium Ion Cells

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State of Health and Charge Estimation Based on Adaptive Boosting integrated with particle swarm optimization/support vector machine (AdaBoost-PSO-SVM) Model for Lithium-ion Batteries

International Journal of Electrochemical Science ◽

10.20964/2022.02.03 ◽

2022 ◽

pp. ArticleID:220212

Author(s):

Ran Li ◽

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Lithium Ion Batteries ◽

Particle Swarm ◽

Lithium Ion ◽

Support Vector ◽

State Of Health ◽

Swarm Optimization ◽

Adaptive Boosting ◽

Svm Model

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state of health
Recently Published Documents

TOTAL DOCUMENTS

H-INDEX

Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling

Principle component analysis-based optimized feature extraction merged with nonlinear regression model for improved state-of-health prediction

An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries

Virtual experiments for battery state of health estimation based on neural networks and in-vehicle data

An estimation model for state of health of lithium-ion batteries using energy-based features

Improved State-of-health prediction based on auto-regressive integrated moving average with exogenous variables model in overcoming battery degradation-dependent internal parameter variation

An encoder-decoder model based on deep learning for state of health estimation of lithium-ion battery

State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network

State of health estimation of cycle aged large format lithium-ion cells based on partial charging

State of Health and Charge Estimation Based on Adaptive Boosting integrated with particle swarm optimization/support vector machine (AdaBoost-PSO-SVM) Model for Lithium-ion Batteries

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state of healthRecently Published Documents

TOTAL DOCUMENTS

H-INDEX

Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling

Principle component analysis-based optimized feature extraction merged with nonlinear regression model for improved state-of-health prediction

An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries

Virtual experiments for battery state of health estimation based on neural networks and in-vehicle data

An estimation model for state of health of lithium-ion batteries using energy-based features

Improved State-of-health prediction based on auto-regressive integrated moving average with exogenous variables model in overcoming battery degradation-dependent internal parameter variation

An encoder-decoder model based on deep learning for state of health estimation of lithium-ion battery

State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network

State of health estimation of cycle aged large format lithium-ion cells based on partial charging

State of Health and Charge Estimation Based on Adaptive Boosting integrated with particle swarm optimization/support vector machine (AdaBoost-PSO-SVM) Model for Lithium-ion Batteries

state of health
Recently Published Documents