Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter
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Yonggang Liu | Jiangwei Shen | Xing Shu | Yuanjian Zhang | Zheng Chen | Hongqian Zhao | Zheng Chen | Xing Shu | Jiangwei Shen | Yuanjian Zhang | Yonggang Liu | Hongqian Zhao
[1] Aviv Segev,et al. On Neural Network Activation Functions and Optimizers in Relation to Polynomial Regression , 2019, 2019 IEEE International Conference on Big Data (Big Data).
[2] Xiaofeng Ding,et al. Analytical and Experimental Evaluation of SiC-Inverter Nonlinearities for Traction Drives Used in Electric Vehicles , 2018, IEEE Transactions on Vehicular Technology.
[3] Dae-Ki Kang,et al. Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network , 2018, Neural Networks.
[4] Kai Wang,et al. Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach , 2020, Complex..
[5] Chunyun Fu,et al. State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter , 2020, Applied Mathematical Modelling.
[6] Qiang Miao,et al. State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network , 2019, Energy.
[7] Hongwen He,et al. An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries , 2019, Applied Energy.
[8] Li Sun,et al. Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer , 2020 .
[9] Ruixin Yang,et al. A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles , 2019 .
[10] 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.
[11] Yujie Wang,et al. A framework for state-of-charge and remaining discharge time prediction using unscented particle filter , 2020 .
[12] 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 .
[13] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[14] Xiaosong Hu,et al. State estimation for advanced battery management: Key challenges and future trends , 2019, Renewable and Sustainable Energy Reviews.
[15] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[16] Weihua Li,et al. State-of-charge estimation of lithium-ion batteries using LSTM and UKF , 2020, Energy.
[17] Jinli Xu,et al. A Multi-Timescale Estimator for Lithium-Ion Battery State of Charge and State of Energy Estimation Using Dual H Infinity Filter , 2019, IEEE Access.
[18] Guang Li,et al. Online diagnosis of state of health for lithium-ion batteries based on short-term charging profiles , 2020 .
[19] Ali Emadi,et al. State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach , 2018, Journal of Power Sources.
[20] Tufan Kumbasar,et al. Learning with Type-2 Fuzzy activation functions to improve the performance of Deep Neural Networks , 2019, Eng. Appl. Artif. Intell..
[21] Fei Xiao,et al. An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit , 2019, Energies.
[22] Hao Mu,et al. A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm , 2016 .
[23] Le Yi Wang,et al. Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles , 2020 .
[24] Cheng Chen,et al. A Lithium-Ion Battery-in-the-Loop Approach to Test and Validate Multiscale Dual H Infinity Filters for State-of-Charge and Capacity Estimation , 2018, IEEE Transactions on Power Electronics.
[25] Chao Huang,et al. Robustness Evaluation of Extended and Unscented Kalman Filter for Battery State of Charge Estimation , 2018, IEEE Access.
[26] 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.
[27] Hicham Chaoui,et al. State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks , 2017, IEEE Transactions on Vehicular Technology.
[28] Shunkun Yang,et al. Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries , 2020 .
[29] K. Muttaqi,et al. State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach , 2020, IEEE Transactions on Industry Applications.
[30] Ruixin Yang,et al. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter , 2019, Journal of Cleaner Production.
[31] Cheng Xu,et al. State of charge and model parameters estimation of liquid metal batteries based on adaptive unscented Kalman filter , 2019, Energy Procedia.
[32] Guang Li,et al. An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation , 2020 .
[33] Jianxiong Zhou,et al. State-of-charge estimation of lithium-ion batteries based on improved H infinity filter algorithm and its novel equalization method , 2020, Journal of Cleaner Production.
[34] Dong Wang,et al. A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile , 2016 .
[35] Zhang Peng,et al. State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter , 2017 .
[36] Song-Yul Choe,et al. An adaptive sigma-point Kalman filter with state equality constraints for online state-of-charge estimation of a Li(NiMnCo)O2/Carbon battery using a reduced-order electrochemical model , 2020 .
[37] Xi Liu,et al. > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .
[38] Jian Ma,et al. A new neural network model for the state-of-charge estimation in the battery degradation process , 2014 .
[39] Michael Pecht,et al. State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation , 2014 .
[40] Guang Li,et al. A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization , 2020 .
[41] Chenming Li,et al. State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles , 2018, Energy.
[42] Guang Li,et al. An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation , 2020 .
[43] E. Schaltz,et al. Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage , 2020, Energies.
[44] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[45] Youren Wang,et al. Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach , 2019, Applied Mathematical Modelling.
[46] M. A. Hannan,et al. State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review , 2019, IEEE Access.
[47] Michael Kurrat,et al. Electrochemical impedance spectroscopy based estimation of the state of charge of lithium-ion batteries , 2016 .
[48] Le Yi Wang,et al. A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter , 2017 .