Validation and verification of a hybrid method for remaining useful life prediction of lithium-ion batteries

Journal of Cleaner Production ◽

10.1016/j.jclepro.2018.12.041 ◽

2019 ◽

Vol 212 ◽

pp. 240-249 ◽

Author(s):

YongZhi Zhang ◽

Rui Xiong ◽

HongWen He ◽

Michael Pecht

Keyword(s):

Lithium Ion Batteries ◽

Hybrid Method ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Validation And Verification ◽

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Remaining Useful Life Assessment for Lithium-ion Batteries using CNN-LSTM-DNN Hybrid Method

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2021.3071622 ◽

2021 ◽

pp. 1-1

Author(s):

Brahim Zraibi ◽

Chafik Okar ◽

Hicham Chaoui ◽

Mohamed Mansouri

Keyword(s):

Lithium Ion Batteries ◽

Hybrid Method ◽

Lithium Ion ◽

Remaining Useful Life ◽

Life Assessment ◽

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A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries

Energy Reports ◽

10.1016/j.egyr.2021.08.182 ◽

2021 ◽

Vol 7 ◽

pp. 5562-5574 ◽

Author(s):

Shunli Wang ◽

Siyu Jin ◽

Dekui Bai ◽

Yongcun Fan ◽

Haotian Shi ◽

...

Keyword(s):

Deep Learning ◽

Lithium Ion Batteries ◽

Critical Review ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Learning Methods ◽

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Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model

Microelectronics Reliability ◽

10.1016/j.microrel.2016.07.151 ◽

2016 ◽

Vol 65 ◽

pp. 265-273 ◽

Author(s):

Yapeng Zhou ◽

Miaohua Huang

Keyword(s):

Lithium Ion Batteries ◽

Empirical Mode Decomposition ◽

Life Prediction ◽

Arima Model ◽

Lithium Ion ◽

Remaining Useful Life ◽

Mode Decomposition ◽

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Capacity Fade Modeling and Remaining Useful Life Prediction of Lithium-ion Batteries Using An Adaptive Degradation Model

10.1109/sdpc52933.2021.9563564 ◽

2021 ◽

Author(s):

Zhaowei Nie ◽

Lianfeng Li ◽

Weiwei Cui ◽

Jing Wang

Keyword(s):

Lithium Ion Batteries ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Capacity Fade ◽

Degradation Model ◽

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Remaining useful life prediction for lithium-ion batteries based on an integrated health indicator

Microelectronics Reliability ◽

10.1016/j.microrel.2018.07.047 ◽

2018 ◽

Vol 88-90 ◽

pp. 1189-1194 ◽

Author(s):

Yongquan Sun ◽

Xueling Hao ◽

Michael Pecht ◽

Yapeng Zhou

Keyword(s):

Lithium Ion Batteries ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Health Indicator ◽

Integrated Health ◽

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Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Network and Bat-Based Particle Filter

IEEE Access ◽

10.1109/access.2019.2913163 ◽

2019 ◽

Vol 7 ◽

pp. 54843-54854 ◽

Author(s):

Yi Wu ◽

Wei Li ◽

Youren Wang ◽

Kai Zhang

Keyword(s):

Neural Network ◽

Particle Filter ◽

Lithium Ion Batteries ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

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Remaining useful life prediction for Lithium-ion batteries using fractional Brownian motion and Fruit-fly Optimization Algorithm

Measurement ◽

10.1016/j.measurement.2020.107904 ◽

2020 ◽

Vol 161 ◽

pp. 107904 ◽

Author(s):

Haiyang Wang ◽

Wanqing Song ◽

Enrico Zio ◽

Aleksey Kudreyko ◽

Yujin Zhang

Keyword(s):

Brownian Motion ◽

Lithium Ion Batteries ◽

Optimization Algorithm ◽

Life Prediction ◽

Lithium Ion ◽

Fruit Fly ◽

Remaining Useful Life ◽

Fruit Fly Optimization Algorithm ◽

Fruit Fly Optimization ◽

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Residual Remaining Useful Life Prediction Method for Lithium-Ion Batteries in Satellite With Incomplete Healthy Historical Data

IEEE Access ◽

10.1109/access.2019.2938060 ◽

2019 ◽

Vol 7 ◽

pp. 127788-127799 ◽

Author(s):

Jian Peng ◽

Zhongbao Zhou ◽

Jiongqi Wang ◽

Di Wu ◽

Yinman Guo

Keyword(s):

Lithium Ion Batteries ◽

Life Prediction ◽

Historical Data ◽

Prediction Method ◽

Lithium Ion ◽

Remaining Useful Life ◽

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

IEEE Access ◽

10.1109/access.2020.2981261 ◽

2020 ◽

Vol 8 ◽

pp. 53307-53320

Author(s):

Danhua Zhou ◽

Zhanying Li ◽

Jiali Zhu ◽

Haichuan Zhang ◽

Lin Hou

Keyword(s):

Lithium Ion Batteries ◽

Health Monitoring ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

State Of Health ◽

Convolutional Network ◽

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Nonlinear-Drifted Fractional Brownian Motion With Multiple Hidden State Variables for Remaining Useful Life Prediction of Lithium-Ion Batteries

IEEE Transactions on Reliability ◽

10.1109/tr.2019.2896230 ◽

2020 ◽

Vol 69 (2) ◽

pp. 768-780 ◽

Author(s):

Heng Zhang ◽

Zhenling Mo ◽

Jianyu Wang ◽

Qiang Miao

Keyword(s):

Brownian Motion ◽

Lithium Ion Batteries ◽

Fractional Brownian Motion ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

State Variables ◽

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