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Xianyong Zhang, Xiaoshun Zhang
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A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries
10.1016/j.est.2024.111524 · doi-reference
Improved multiple feature-electrochemical thermal coupling modeling of lithium-ion batteries at low-temperature with real-time coefficient correction
10.23919/pcmp.2023.000257 · doi-reference
Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries
10.1016/j.ress.2022.108920 · doi-reference
Improved hyperparameter Bayesian optimization-bidirectional long short-term memory optimization for high-precision battery state of charge estimation
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SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture
10.1016/j.energy.2022.124612 · doi-reference
Transfer learning-based state of charge and state of health estimation for Li-ion batteries: a review
10.1109/tte.2023.3293551 · doi-reference
SOC estimation for lithium-ion batteries based on BiGRU with SE attention and Savitzky-Golay filter
10.1016/j.est.2024.111930 · doi-reference
Electrical lithium-ion battery models based on recurrent neural networks: a holistic approach
10.1016/j.est.2022.106461 · doi-reference
Review of battery state estimation methods for electric vehicles-part I: SOC estimation
10.1016/j.est.2024.111435 · doi-reference
Machine learning of materials design and state prediction for lithium ion batteries
10.1016/j.cjche.2021.04.009 · doi-reference
Exploiting artificial neural networks for the state of charge estimation in ev/hv battery systems: a review
10.3390/batteries11030107 · doi-reference
State-of-charge estimation of lithium ion batteries based on adaptive iterative extended Kalman filter
10.1016/j.est.2021.102593 · doi-reference
Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter
10.1016/j.energy.2020.119233 · doi-reference
State of charge estimation for lithium-ion battery based on an intelligent adaptive extended Kalman filter with improved noise estimator
10.1016/j.energy.2020.119025 · doi-reference
Lithium-ion battery parameters and state-of-charge joint estimation based on H-infinity and unscented Kalman filters
10.1109/tvt.2017.2709326 · doi-reference
A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter
10.1016/j.apenergy.2016.12.133 · doi-reference
Electrochemical impedance correlation analysis for the estimation of Li-ion battery state of charge, state of health and internal temperature
10.1016/j.est.2022.104608 · doi-reference
Advances in batteries, battery modeling, battery management system, battery thermal management, SOC, SOH, and charge/discharge characteristics in EV applications
10.1109/access.2023.3318121 · doi-reference
Open-circuit voltage models for battery management systems: a review
10.3390/en15186803 · doi-reference
A review of battery energy storage systems and advanced battery management system for different applications: challenges and recommendations
10.1016/j.est.2024.111179 · doi-reference
A review of lithium-ion battery thermal runaway modeling and diagnosis approaches
10.3390/pr10061192 · doi-reference
A novel method of parameter identification and state of charge estimation for lithium-ion battery energy storage system
10.1016/j.est.2022.104124 · doi-reference
An analysis of China's energy policy from 1981 to 2020: transitioning towards to a diversified and low-carbon energy system
10.1016/j.enpol.2022.112806 · doi-reference
Long-term electric vehicles outlook and their potential impact on electric grid
10.1016/j.enpol.2019.111103 · doi-reference
A comprehensive review of the key technologies for pure electric vehicles
10.1016/j.energy.2019.06.077 · doi-reference