Battery remaining useful life prediction algorithm based on support vector regression and unscented particle filter
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Yong Zhou | Yang Yu | Chao Zhang | Xi Peng | Chao Zhang | Xi Peng | Yang Yu | Yong Zhou
[1] David He,et al. Lithium-ion battery life prognostic health management system using particle filtering framework , 2011 .
[2] Min Li,et al. Prognostics of Lithium-Ion Batteries Based on the Verhulst Model, Particle Swarm Optimization and Particle Filter , 2014, IEEE Transactions on Instrumentation and Measurement.
[3] Du Junwei,et al. The improved algorithm of UPF in spacecraft autonomous optical navigation , 2008, 2008 27th Chinese Control Conference.
[4] Jay Lee,et al. A review on prognostics and health monitoring of Li-ion battery , 2011 .
[5] Yu Peng,et al. Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).
[6] Jianbo Yu,et al. State-of-Health Monitoring and Prediction of Lithium-Ion Battery Using Probabilistic Indication and State-Space Model , 2015, IEEE Transactions on Instrumentation and Measurement.
[7] Charalambos D. Charalambous,et al. Estimation of mobile station position and velocity in multipath wireless networks using the unscented particle filter , 2007, 2007 46th IEEE Conference on Decision and Control.
[8] Michael Osterman,et al. Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .
[9] Huajing Fang,et al. An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction , 2015, Reliab. Eng. Syst. Saf..
[10] Kai Goebel,et al. Comparison of prognostic algorithms for estimating remaining useful life of batteries , 2009 .
[11] Bhaskar Saha,et al. Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.
[12] Hongsup Lim,et al. Factors that affect cycle-life and possible degradation mechanisms of a Li-ion cell based on LiCoO2 , 2002 .
[13] Fang Hua-jing,et al. Cycle life prediction of lithium-ion batteries based on SIR particle filtering , 2013, 2013 25th Chinese Control and Decision Conference (CCDC).
[14] Xiaoning Jin,et al. Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter , 2014 .
[15] Kwok-Leung Tsui,et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..
[16] Chao Zhang,et al. Prediction of remaining useful life of battery cell using logistic regression based on strong tracking particle filter , 2015, 2015 IEEE Conference on Prognostics and Health Management (PHM).
[17] Wei Liang,et al. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..
[18] K. Goebel,et al. An integrated approach to battery health monitoring using bayesian regression and state estimation , 2007, 2007 IEEE Autotestcon.
[19] Wei Xie,et al. An Integrated Probabilistic Approach to Lithium-Ion Battery Remaining Useful Life Estimation , 2015, IEEE Transactions on Instrumentation and Measurement.
[20] Webb L. Burgess. Valve Regulated Lead Acid battery float service life estimation using a Kalman filter , 2009 .
[21] E. Ying. Application of Support Vector Regression Algorithm in Colleges Recruiting Students Prediction , 2012, 2012 International Conference on Computer Science and Electronics Engineering.
[22] Chen Chen,et al. An Adaptive Unscented Particle Filter for Tracking Ground Maneuvering Target , 2007, 2007 International Conference on Mechatronics and Automation.