A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries
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Ning Ma | Jian Ma | Yu Ding | Jinwen Sun | Chen Lu | Laifa Tao | Yuzhuan Su | Jin Chong | Haizu Jin | Yongshou Lin | Yujie Cheng | Pengchao Shang | Xinyu Zou | Chen Lu | Jian Ma | Ning Ma | Jinwen Sun | Xinyu Zou | Yujie Cheng | Yuzhuan Su | Jin Chong | Haizu Jin | Yongshou Lin | Laifa Tao | Yu Ding | Pengchao Shang
[1] Hyunbo Cho,et al. Improving memory-based collaborative filtering via similarity updating and prediction modulation , 2010, Inf. Sci..
[2] M. Broussely,et al. Aging mechanism in Li ion cells and calendar life predictions , 2001 .
[3] Qun Dai,et al. A novel transfer learning framework for time series forecasting , 2018, Knowl. Based Syst..
[4] Andrew V. Knyazev,et al. Angles between subspaces and their tangents , 2012, J. Num. Math..
[5] Huajing Fang,et al. A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery , 2017 .
[6] B. Kalaiselvi,et al. An efficient Pearson correlation based improved random forest classification for protein structure prediction techniques , 2020 .
[7] Qinghua Hu,et al. Transfer learning for short-term wind speed prediction with deep neural networks , 2016 .
[8] N. Omar,et al. Lithium iron phosphate based battery: Assessment of the aging parameters and development of cycle life model , 2014 .
[9] Ali Emadi,et al. Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries , 2018, IEEE Transactions on Industrial Electronics.
[10] Jiuchun Jiang,et al. Aging mechanisms under different state-of-charge ranges and the multi-indicators system of state-of-health for lithium-ion battery with Li(NiMnCo)O2 cathode , 2018, Journal of Power Sources.
[11] Yonggui Dong,et al. A cosine similarity-based negative selection algorithm for time series novelty detection , 2006 .
[12] W. Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .
[13] Dongrui Wu,et al. Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis , 2019, Neurocomputing.
[14] M. Edenius,et al. QPANDA: an advanced nodal method for LWR analyses , 1985 .
[15] David Zhang,et al. Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization , 2016, IEEE Transactions on Cybernetics.
[16] M. Wohlfahrt‐Mehrens,et al. Temperature dependent ageing mechanisms in Lithium-ion batteries – A Post-Mortem study , 2014 .
[17] Azah Mohamed,et al. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations , 2017 .
[18] Xiaosong Hu,et al. A comparative study of equivalent circuit models for Li-ion batteries , 2012 .
[19] Ricardo J. Bessa,et al. On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power , 2016 .
[20] Kai Wang,et al. Remaining useful life prediction for supercapacitor based on long short-term memory neural network , 2019, Journal of Power Sources.
[21] Kristen A. Severson,et al. Data-driven prediction of battery cycle life before capacity degradation , 2019, Nature Energy.
[22] Mohd Yamani Idna Idris,et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries , 2019, Renewable and Sustainable Energy Reviews.
[23] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[24] Yang Gao,et al. Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method , 2019 .
[25] Yi Xie,et al. A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model , 2019, Applied Energy.
[26] Taejung Yeo,et al. Recursive Bayesian filtering framework for lithium-ion cell state estimation , 2016 .
[27] Michael Pecht,et al. Accelerated degradation model for C-rate loading of lithium-ion batteries , 2019, International Journal of Electrical Power & Energy Systems.
[28] Abdullatif Ben-Nakhi,et al. Cooling load prediction for buildings using general regression neural networks , 2004 .
[29] Ganesan Nagasubramanian,et al. Accelerated power degradation of Li-ion cells , 2003 .
[30] Josef Kittler,et al. A decision cognizant Kullback-Leibler divergence , 2017, Pattern Recognit..
[31] Chen Lu,et al. Performance Assessment and Prediction for Superheterodyne Receivers Based on Mahalanobis Distance and Time Sequence Analysis , 2017 .
[32] Xu Liu,et al. Pose-dependent tool tip dynamics prediction using transfer learning , 2019, International Journal of Machine Tools and Manufacture.
[33] Michael Osterman,et al. Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .
[34] Leandro Pardo,et al. THE JENSEN-SHANNON DIVERGENCE , 1997 .
[35] Delphine Riu,et al. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .
[36] Hongwen He,et al. Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries , 2018, IEEE Transactions on Vehicular Technology.
[37] Guangzhong Dong,et al. Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter , 2017 .
[38] Girish Chowdhary,et al. Cross-Domain Transfer in Reinforcement Learning Using Target Apprentice , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[39] F. Cadini,et al. State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters , 2019, Applied Energy.
[40] Sivaraman Balakrishnan,et al. Optimal kernel choice for large-scale two-sample tests , 2012, NIPS.
[41] Taejung Yeo,et al. A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation , 2015 .
[42] Philip S. Yu,et al. Text classification without negative examples revisit , 2006, IEEE Transactions on Knowledge and Data Engineering.
[43] Maxime Montaru,et al. From a novel classification of the battery state of charge estimators toward a conception of an ideal one , 2015 .
[44] M. Winter,et al. Performance and cost of materials for lithium-based rechargeable automotive batteries , 2018 .
[45] Weijun Gu,et al. A new method of accelerated life testing based on the Grey System Theory for a model-based lithium-ion battery life evaluation system , 2014 .
[46] Miriam A. M. Capretz,et al. Transfer learning with seasonal and trend adjustment for cross-building energy forecasting , 2018 .
[47] Manoj Kumar Tiwari,et al. An efficient recommendation generation using relevant Jaccard similarity , 2019, Inf. Sci..
[48] Chen Yang,et al. Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery , 2017, Microelectron. Reliab..
[49] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[50] Boris G. Mirkin,et al. The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning , 2017, Pattern Recognit..