Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression

Neurocomputing ◽

10.1016/j.neucom.2019.09.074 ◽

2020 ◽

Vol 376 ◽

pp. 95-102 ◽

Author(s):

Zhiwei Xue ◽

Yong Zhang ◽

Cheng Cheng ◽

Guijun Ma

Keyword(s):

Kalman Filter ◽

Support Vector Regression ◽

Lithium Ion Batteries ◽

Life Prediction ◽

Unscented Kalman Filter ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

Adaptive Unscented Kalman Filter ◽

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Remaining Useful Life Prediction of Lithium-Ion Batteries Using Support Vector Regression Optimized by Artificial Bee Colony

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2019.2932605 ◽

2019 ◽

Vol 68 (10) ◽

pp. 9543-9553 ◽

Author(s):

Yingzhou Wang ◽

Yulong Ni ◽

Shuai Lu ◽

Jianguo Wang ◽

Xiuyu Zhang

Keyword(s):

Support Vector Regression ◽

Lithium Ion Batteries ◽

Life Prediction ◽

Artificial Bee Colony ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

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Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Cataclysmic Mutation Genetic Algorithm and Support Vector Regression

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) ◽

10.1109/icpeca51329.2021.9362683 ◽

2021 ◽

Author(s):

Xin Chen ◽

Mingqing Xiao ◽

Bincheng Wen

Keyword(s):

Genetic Algorithm ◽

Support Vector Regression ◽

Lithium Ion Batteries ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

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

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2017.2782224 ◽

2018 ◽

Vol 65 (7) ◽

pp. 5634-5643 ◽

Author(s):

Jingwen Wei ◽

Guangzhong Dong ◽

Zonghai Chen

Keyword(s):

Particle Filter ◽

Support Vector Regression ◽

Lithium Ion Batteries ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

State Of Health ◽

Useful Life ◽

Health Diagnosis

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A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) ◽

10.1109/icphm.2016.7542847 ◽

2016 ◽

Author(s):

Baohua Mo ◽

Jingsong Yu ◽

Diyin Tang ◽

Hao Liu ◽

Jingsong Yu

Keyword(s):

Kalman Filter ◽

Particle Filter ◽

Lithium Ion Batteries ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Useful Life ◽

Prediction Approach

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Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter

Journal of Power Sources ◽

10.1016/j.jpowsour.2014.07.176 ◽

2014 ◽

Vol 271 ◽

pp. 114-123 ◽

Author(s):

Hancheng Dong ◽

Xiaoning Jin ◽

Yangbing Lou ◽

Changhong Wang

Keyword(s):

Particle Filter ◽

Support Vector Regression ◽

Lithium Ion Battery ◽

Health Monitoring ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

State Of Health ◽

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Remaining Useful Life Prediction of Lithium-ion Battery Based on Unscented Kalman Filter and Back propagation Neural Network

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls.2019.8908952 ◽

2019 ◽

Author(s):

Zhouxiao Xiao ◽

Huajing Fang ◽

Zheng Li ◽

Yang Chang

Keyword(s):

Neural Network ◽

Kalman Filter ◽

Lithium Ion Battery ◽

Life Prediction ◽

Unscented Kalman Filter ◽

Back Propagation ◽

Lithium Ion ◽

Back Propagation Neural Network ◽

Remaining Useful Life ◽

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A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries

Microelectronics Reliability ◽

10.1016/j.microrel.2018.04.007 ◽

2018 ◽

Vol 85 ◽

pp. 99-108 ◽

Author(s):

Qi Zhao ◽

Xiaoli Qin ◽

Hongbo Zhao ◽

Wenquan Feng

Keyword(s):

Support Vector Regression ◽

Lithium Ion Batteries ◽

Prediction Method ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

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Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Empirical Model and Improved Least Squares Support Vector Machine

10.1007/978-981-16-6372-7_6 ◽

2021 ◽

pp. 47-55

Author(s):

Wan Chen ◽

Yanping Cai ◽

Aihua Li ◽

Yanzhao Su ◽

Ke Jiang

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Lithium Ion Batteries ◽

Empirical Model ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

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A method based on improved ant lion optimization and support vector regression for remaining useful life estimation of lithium‐ion batteries

Energy Science & Engineering ◽

10.1002/ese3.460 ◽

2019 ◽

Vol 7 (6) ◽

pp. 2797-2813 ◽

Author(s):

Yingzhou Wang ◽

Yulong Ni ◽

Na Li ◽

Shuai Lu ◽

Shude Zhang ◽

...

Keyword(s):

Support Vector Regression ◽

Lithium Ion Batteries ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

Life Estimation ◽

Ant Lion Optimization ◽

Ant Lion ◽

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Prediction of the Remaining Useful Life of Lithium-ion Batteries Based on Dempster-Shafer Theory and the Support Vector Regression-particle Filter

IEEE Access ◽

10.1109/access.2021.3136131 ◽

2021 ◽

pp. 1-1

Author(s):

Hancheng Dong

Keyword(s):

Particle Filter ◽

Support Vector Regression ◽

Lithium Ion Batteries ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

Dempster Shafer Theory ◽

Useful Life ◽

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