Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks
暂无分享,去创建一个
Mihai V. Micea | Gabriel N. Carstoiu | Lucian Ungurean | Gabriel Cârstoiu | M. Micea | Lucian Ungurean
[1] Minho Kim,et al. Estimation of Li-ion Battery State of Health based on Multilayer Perceptron: as an EV Application , 2018 .
[2] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[3] Mihai V. Micea,et al. Battery state of health estimation: a structured review of models, methods and commercial devices , 2017 .
[4] Christian Fleischer,et al. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .
[5] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[6] John McPhee,et al. A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation , 2014 .
[7] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[8] Li Lin,et al. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks , 2018, Neurocomputing.
[9] 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.
[10] M. A. Hannan,et al. A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations , 2018, Journal of Cleaner Production.
[11] Sebastian Ruder,et al. An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.
[12] Yuehua Cheng,et al. Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery , 2018, Applied Sciences.
[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] Rui Xiong,et al. A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).
[15] Wansu Lim,et al. Hybrid VARMA and LSTM Method for Lithium-ion Battery State-of-Charge and Output Voltage Forecasting in Electric Motorcycle Applications , 2019, IEEE Access.
[16] Alberto Quattrini Li,et al. Deep Neural Networks: A Comparison on Different Computing Platforms , 2018, 2018 15th Conference on Computer and Robot Vision (CRV).
[17] Guangzhong Dong,et al. Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering , 2019, International Journal of Energy Research.
[18] Qiang Miao,et al. State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network , 2019, Energy.
[19] Joeri Van Mierlo,et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review , 2019, Renewable and Sustainable Energy Reviews.
[20] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[21] Jürgen Schmidhuber,et al. LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[22] I. Villarreal,et al. Critical review of state of health estimation methods of Li-ion batteries for real applications , 2016 .
[23] Feng Liu,et al. A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery , 2019, IEEE Access.
[24] 구동영,et al. LSTM(Long Short-Term Memory)을 이용한 가짜 리뷰 생성과 분석 및 평가 , 2019 .
[25] Kwok-Leung Tsui,et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..
[26] Lei Zhang,et al. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks , 2019, Journal of Energy Storage.
[27] Thomas M. Jahns,et al. A compact unified methodology via a recurrent neural network for accurate modeling of lithium-ion battery voltage and state-of-charge , 2017, 2017 IEEE Energy Conversion Congress and Exposition (ECCE).