State of charge estimation for lithium-ion batteries using gated recurrent unit recurrent neural network and adaptive Kalman filter
出版年份 2022 全文链接
标题
State of charge estimation for lithium-ion batteries using gated recurrent unit recurrent neural network and adaptive Kalman filter
作者
关键词
-
出版物
Journal of Energy Storage
Volume 55, Issue -, Pages 105396
出版商
Elsevier BV
发表日期
2022-08-05
DOI
10.1016/j.est.2022.105396
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- State-of-charge estimation tolerant of battery aging based on a physics-based model and an adaptive cubature Kalman filter
- (2021) Xiaoyu Li et al. ENERGY
- State-of-charge estimation of lithium-ion battery pack by using an adaptive extended Kalman filter for electric vehicles
- (2021) Zhiyong Zhang et al. Journal of Energy Storage
- State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach
- (2021) Jinpeng Tian et al. APPLIED ENERGY
- State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network
- (2021) Junxiong Chen et al. ENERGY
- A novel state of charge estimation method for lithium-ion batteries based on bias compensation
- (2021) Tiancheng Ouyang et al. ENERGY
- A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles
- (2021) Zuolu Wang et al. Energy Reports
- A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter
- (2020) Yong Tian et al. APPLIED ENERGY
- Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model
- (2020) Fei Feng et al. JOURNAL OF POWER SOURCES
- A GRU-RNN based momentum optimized algorithm for SOC estimation
- (2020) Meng Jiao et al. JOURNAL OF POWER SOURCES
- State-of-charge estimation of lithium-ion batteries using LSTM and UKF
- (2020) Fangfang Yang et al. ENERGY
- Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression
- (2020) Zhongwei Deng et al. ENERGY
- State of charge estimation of lithium-ion batteries using hybrid autoencoder and Long Short Term Memory neural networks
- (2020) Mohammad Fasahat et al. JOURNAL OF POWER SOURCES
- State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator
- (2020) Daoming Sun et al. ENERGY
- Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends
- (2020) M.S. Hossain Lipu et al. JOURNAL OF CLEANER PRODUCTION
- Improving the state of charge estimation of reused lithium-ion batteries by abating hysteresis using machine learning technique
- (2020) Zhicheng Xu et al. Journal of Energy Storage
- State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
- (2019) Fangfang Yang et al. ENERGY
- Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks
- (2019) Jichao Hong et al. APPLIED ENERGY
- State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter
- (2019) Cheng Chen et al. JOURNAL OF CLEANER PRODUCTION
- Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries
- (2019) Prashant Shrivastava et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter
- (2018) Linfeng Zheng et al. ENERGY
- Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
- (2018) Ephrem Chemali et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach
- (2018) Ephrem Chemali et al. JOURNAL OF POWER SOURCES
- A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique
- (2017) Yanwen Li et al. ENERGY
- State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
- (2017) Hicham Chaoui et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- On state-of-charge determination for lithium-ion batteries
- (2017) Zhe Li et al. JOURNAL OF POWER SOURCES
- A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations
- (2017) M.A. Hannan et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation
- (2014) Wei He et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures
- (2013) Yinjiao Xing et al. APPLIED ENERGY
- Advancements in OCV Measurement and Analysis for Lithium-Ion Batteries
- (2013) Mathias Petzl et al. IEEE TRANSACTIONS ON ENERGY CONVERSION
- Support Vector Machines Used to Estimate the Battery State of Charge
- (2013) Juan Carlos Alvarez Anton et al. IEEE TRANSACTIONS ON POWER ELECTRONICS
- Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model
- (2011) Long Xu et al. ENERGY CONVERSION AND MANAGEMENT
- State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF
- (2010) Mohammad Charkhgard et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries
- (2009) Kong Soon Ng et al. APPLIED ENERGY
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