A mean-covariance decomposition method for battery capacity prognostics
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Jian Guo | Zhaojun Li | Shuai Yang | Jian Guo | Z. Li | Shuai Yang | Jian Guo | Shuai Yang
[1] T. Weigert,et al. State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks , 2011 .
[2] Julian de Hoog,et al. A Multi-Factor Battery Cycle Life Prediction Methodology for Optimal Battery Management , 2015, e-Energy.
[3] Yuang-Shung Lee,et al. A Merged Fuzzy Neural Network and Its Applications in Battery State-of-Charge Estimation , 2007, IEEE Transactions on Energy Conversion.
[4] Wei He,et al. State of charge estimation for electric vehicle batteries using unscented kalman filtering , 2013, Microelectron. Reliab..
[5] M. Safari,et al. Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries , 2009 .
[6] Vladimir Vapnik,et al. Support-vector networks , 2004, Machine Learning.
[7] Bo-Suk Yang,et al. Intelligent prognostics for battery health monitoring based on sample entropy , 2011, Expert Syst. Appl..
[8] Jian Zhang,et al. Phenomenologically modeling the formation and evolution of the solid electrolyte interface on the graphite electrode for lithium-ion batteries , 2008 .
[9] Puqiang Zhang,et al. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery , 2014 .
[10] Bhaskar Saha,et al. Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.
[11] Hongwen He,et al. A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles , 2014 .
[12] Roumiana Tsenkova,et al. Computational simulations and a practical application of moving-window two-dimensional correlation spectroscopy , 2006 .
[13] Yu Peng,et al. Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression , 2013, Microelectron. Reliab..
[14] D. Dunson,et al. Random Effects Selection in Linear Mixed Models , 2003, Biometrics.
[15] Siem Jan Koopman,et al. A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations , 2010 .
[16] K. T. Chau,et al. A new battery available capacity indicator for electric vehicles using neural network , 2002 .
[17] Gregory L. Plett,et al. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .
[18] Chenghui Zhang,et al. Estimation of battery state-of-charge using ν-support vector regression algorithm , 2008 .
[19] W. Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .
[20] M. Pourahmadi. Joint mean-covariance models with applications to longitudinal data: Unconstrained parameterisation , 1999 .
[21] Gregory L. Plett,et al. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation , 2006 .
[22] M. Wohlfahrt‐Mehrens,et al. Ageing mechanisms in lithium-ion batteries , 2005 .
[23] R. Rebonato,et al. The most general methodology for creating a valid correlation matrix for risk management and option pricing purposes , 2000 .
[24] Matthew B. Pinson,et al. Theory of SEI Formation in Rechargeable Batteries: Capacity Fade, Accelerated Aging and Lifetime Prediction , 2012, 1210.3672.
[25] Jean-Michel Vinassa,et al. Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks , 2012 .
[26] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[27] Xiaosong Hu,et al. Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for elec , 2011 .
[28] Yuang-Shung Lee,et al. Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems , 2008, IEEE Transactions on Industrial Electronics.
[29] Chao Hu,et al. Online estimation of lithium-ion battery capacity using sparse Bayesian learning , 2015 .
[30] Bo-Hyung Cho,et al. Li-Ion Battery SOC Estimation Method based on the Reduced Order Extended Kalman Filtering , 2006 .
[31] M. Pecht,et al. A Bayesian approach for Li-Ion battery capacity fade modeling and cycles to failure prognostics , 2015 .
[32] Chenlei Leng,et al. A joint modelling approach for longitudinal studies , 2015 .
[33] Krishna R. Pattipati,et al. System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[34] Zhongbao Zhou,et al. A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries in spacecraft , 2013, Reliab. Eng. Syst. Saf..
[35] R. Kohn,et al. Parsimonious Covariance Matrix Estimation for Longitudinal Data , 2002 .
[36] Hurng-Liahng Jou,et al. Auxiliary diagnosis method for lead–acid battery health based on sample entropy , 2009 .
[37] Seongjun Lee,et al. Complementary Cooperation Algorithm Based on DEKF Combined With Pattern Recognition for SOC/Capacity Estimation and SOH Prediction , 2012, IEEE Transactions on Power Electronics.
[38] Douglas M. Bates,et al. Unconstrained parametrizations for variance-covariance matrices , 1996, Stat. Comput..
[39] Robert Leconte,et al. Efficient stochastic generation of multi-site synthetic precipitation data , 2007 .
[40] Michael Buchholz,et al. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods , 2013 .
[41] Seongjun Lee,et al. State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .
[42] Gan Ning,et al. Capacity fade study of lithium-ion batteries cycled at high discharge rates , 2003 .
[43] Kai Goebel,et al. Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .
[44] Cao Binggang,et al. State of charge estimation based on evolutionary neural network , 2008 .
[45] Mark W. Verbrugge,et al. Battery Cycle Life Prediction with Coupled Chemical Degradation and Fatigue Mechanics , 2012 .
[46] Michael Osterman,et al. Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .
[47] Nan M. Laird,et al. Using the General Linear Mixed Model to Analyse Unbalanced Repeated Measures and Longitudinal Data , 1997 .
[48] Terry Hansen,et al. Support vector based battery state of charge estimator , 2005 .
[49] Jae Sik Chung,et al. A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation , 2010 .