Combined Sparse Bayesian Learning Strategy for Remaining Useful Life Forecasting of Lithium-ion Battery

2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control ◽

10.1109/imccc.2012.113 ◽

2012 ◽

Author(s):

Jianbao Zhou ◽

Datong Liu ◽

Yu Peng ◽

Xiyuan Peng

Keyword(s):

Lithium Ion Battery ◽

Bayesian Learning ◽

Learning Strategy ◽

Lithium Ion ◽

Remaining Useful Life ◽

Sparse Bayesian Learning ◽

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An optimized Relevance Vector Machine with incremental learning strategy for lithium-ion battery remaining useful life estimation

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) ◽

10.1109/i2mtc.2013.6555479 ◽

2013 ◽

Author(s):

Jianbao Zhou ◽

Datong Liu ◽

Yu Peng ◽

Xiyuan Peng

Keyword(s):

Lithium Ion Battery ◽

Incremental Learning ◽

Learning Strategy ◽

Lithium Ion ◽

Relevance Vector Machine ◽

Remaining Useful Life ◽

Life Estimation ◽

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Prediction of Remaining Useful Life of Lithium-ion Battery Based on UKF

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9327244 ◽

2020 ◽

Author(s):

Mengtao Huang ◽

Qibo Zhang

Keyword(s):

Lithium Ion Battery ◽

Lithium Ion ◽

Remaining Useful Life ◽

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Remaining useful life prediction for lithium-ion battery by combining an improved particle filter with sliding-window gray model

Energy Reports ◽

10.1016/j.egyr.2020.07.026 ◽

2020 ◽

Vol 6 ◽

pp. 2086-2093

Author(s):

Lin Chen ◽

Jingjing An ◽

Huimin Wang ◽

Mo Zhang ◽

Haihong Pan

Keyword(s):

Particle Filter ◽

Lithium Ion Battery ◽

Life Prediction ◽

Lithium Ion ◽

Sliding Window ◽

Remaining Useful Life ◽

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Novel Statistical Analysis Approach for Remaining Useful Life Prediction of Lithium-Ion Battery

10.1109/icccnt51525.2021.9579982 ◽

2021 ◽

Author(s):

Yash S. Jain ◽

Atharva D. Veer ◽

Gaurav S. Sawant ◽

Yash R. Jain ◽

Sandeep S. Udmale

Keyword(s):

Statistical Analysis ◽

Lithium Ion Battery ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Analysis Approach ◽

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A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery

Reliability Engineering & System Safety ◽

10.1016/j.ress.2021.108082 ◽

2021 ◽

pp. 108082

Author(s):

Ting Tang ◽

Huimei Yuan

Keyword(s):

Neural Network ◽

Lithium Ion Battery ◽

Life Prediction ◽

Hybrid Approach ◽

Lithium Ion ◽

Remaining Useful Life ◽

Decomposition Algorithm ◽

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Remaining useful life prediction of lithium-ion battery with unscented particle filter technique

Microelectronics Reliability ◽

10.1016/j.microrel.2012.12.004 ◽

2013 ◽

Vol 53 (6) ◽

pp. 805-810 ◽

Author(s):

Qiang Miao ◽

Lei Xie ◽

Hengjuan Cui ◽

Wei Liang ◽

Michael Pecht

Keyword(s):

Particle Filter ◽

Lithium Ion Battery ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Filter Technique ◽

Unscented Particle Filter ◽

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Remaining useful life prediction of lithium‐ion battery based on extended Kalman particle filter

International Journal of Energy Research ◽

10.1002/er.5002 ◽

2019 ◽

Vol 44 (3) ◽

pp. 1724-1734 ◽

Author(s):

Bin Duan ◽

Qi Zhang ◽

Fei Geng ◽

Chenghui Zhang

Keyword(s):

Particle Filter ◽

Lithium Ion Battery ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

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Lithium-ion battery Remaining Useful Life prediction based on grey Support Vector Machines

Safety and Reliability of Complex Engineered Systems ◽

10.1201/b19094-272 ◽

2015 ◽

pp. 2089-2094

Author(s):

J Miao ◽

X Li ◽

J Liu ◽

C Peng

Keyword(s):

Support Vector Machines ◽

Lithium Ion Battery ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

Vector Machines ◽

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Remaining Useful Life Prediction of Lithium-ion Battery Based on Discrete Wavelet Transform

Energy Procedia ◽

10.1016/j.egypro.2017.03.582 ◽

2017 ◽

Vol 105 ◽

pp. 2053-2058 ◽

Author(s):

Yujie Wang ◽

Rui Pan ◽

Duo Yang ◽

Xiaopeng Tang ◽

Zonghai Chen

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Lithium Ion Battery ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Discrete Wavelet ◽

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A Comparative Analysis of Lithium Ion Battery Input Profiles for Remaining Useful Life Prediction by Cascade Forward Neural Network

2021 IEEE World AI IoT Congress (AIIoT) ◽

10.1109/aiiot52608.2021.9454234 ◽

2021 ◽

Author(s):

Shaheer Ansari ◽

Afida Ayob ◽

M.S. Hossain Lipu ◽

Mohamad Hanif Md Saad ◽

Aini Hussain

Keyword(s):

Neural Network ◽

Comparative Analysis ◽

Lithium Ion Battery ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

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