Lithium-Ion Battery Remaining Useful Life Prognostics Using Data-Driven Deep Learning Algorithm
暂无分享,去创建一个
Yu Peng | Datong Liu | Lyu Li | Yuchen Song | Yu Peng | Datong Liu | Yuchen Song | Lyu Li
[1] Geoffrey E. Hinton,et al. Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[2] Qiang Miao,et al. Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .
[3] Michael Buchholz,et al. State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation , 2011 .
[4] Datong Liu,et al. Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning , 2015 .
[5] Xiao-Lei Zhang,et al. Deep Belief Networks Based Voice Activity Detection , 2013, IEEE Transactions on Audio, Speech, and Language Processing.
[6] 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.
[7] Chen Yang,et al. Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery , 2017, Microelectron. Reliab..
[8] Yandong Hou,et al. Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm , 2017 .
[9] Jie Liu,et al. Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm , 2013, Neural Computing and Applications.
[10] Jean-Michel Vinassa,et al. Remaining useful life prediction of lithium batteries in calendar ageing for automotive applications , 2012, Microelectron. Reliab..
[11] Yu Peng,et al. A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[12] Zhen Liu,et al. An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries , 2013, Microelectron. Reliab..
[13] Xing Zhao,et al. Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[14] Michael Pecht,et al. Physics-of-failure-based prognostics for electronic products , 2009 .
[15] Jin Cui,et al. Multi-bearing remaining useful life collaborative prediction: A deep learning approach , 2017 .
[16] Kay Chen Tan,et al. Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[17] Miaohua Huang,et al. Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model , 2016, Microelectron. Reliab..
[18] Yu Peng,et al. Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression , 2013, Microelectron. Reliab..
[19] Wenjing Jin,et al. Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment , 2016, IEEE Transactions on Industrial Electronics.
[20] Xin Zhang,et al. Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC , 2017, Microelectron. Reliab..
[21] Xiang Li,et al. Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..
[22] Brigitte Chebel-Morello,et al. ÕExperience Based Approach for Li-ion Batteries RUL PredictionÕ , 2015 .
[23] Nicolas Le Roux,et al. Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.
[24] Wei Liang,et al. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..
[25] Zhixing Wang,et al. Electrochemical analysis for cycle performance and capacity fading of lithium manganese oxide spinel cathode at elevated temperature using p-toluenesulfonyl isocyanate as electrolyte additive , 2015 .
[26] M. Wohlfahrt‐Mehrens,et al. Ageing mechanisms in lithium-ion batteries , 2005 .
[27] Li Lin,et al. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks , 2018, Neurocomputing.
[28] Geoffrey E. Hinton. Deep belief networks , 2009, Scholarpedia.
[29] Brendan McCane,et al. Deep Networks are Effective Encoders of Periodicity , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[30] Bo Liu,et al. Lithium and lithium ion batteries for applications in microelectronic devices: A review , 2015 .
[31] Michael Buchholz,et al. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods , 2013 .