A novel parameter adaptive method for state of charge estimation of aged lithium batteries
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Title
A novel parameter adaptive method for state of charge estimation of aged lithium batteries
Authors
Keywords
Lithium-ion batteries, State of charge, Open circuit voltage, Parameter adaptive, Back-propagation neural network, Improved recursive least squares
Journal
Journal of Energy Storage
Volume 44, Issue -, Pages 103389
Publisher
Elsevier BV
Online
2021-10-19
DOI
10.1016/j.est.2021.103389
References
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- (2020) Fangfang Yang et al. ENERGY
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- (2020) Chaofan Yang et al. Journal of Energy Storage
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- (2019) Jiankun Peng 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
- A review on prognostics and health management (PHM) methods of lithium-ion batteries
- (2019) Huixing Meng et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks
- (2019) Shuzhi Zhang et al. Journal of Energy Storage
- Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm
- (2018) Mahammad A. Hannan et al. IEEE Access
- A parameter adaptive method with dead zone for state of charge and parameter estimation of lithium-ion batteries
- (2018) Feng Guo et al. JOURNAL OF POWER SOURCES
- Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks
- (2017) Claudio Sbarufatti 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
- A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states
- (2016) S. Nejad et al. JOURNAL OF POWER SOURCES
- Modelling and experimental evaluation of parallel connected lithium ion cells for an electric vehicle battery system
- (2016) Thomas Bruen et al. JOURNAL OF POWER SOURCES
- Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter
- (2015) Gustavo Pérez et al. APPLIED ENERGY
- A GA optimization for lithium–ion battery equalization based on SOC estimation by NN and FLC
- (2015) ShuMei Zhang et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles
- (2014) Lei Pei et al. ENERGY
- A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles
- (2013) Rui Xiong et al. ENERGY
- A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries
- (2012) Yao He et al. APPLIED ENERGY
- Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach
- (2011) Hongwen He et al. Energies
- State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model
- (2011) Hongwen He et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- A comparative study of equivalent circuit models for Li-ion batteries
- (2011) Xiaosong Hu et al. JOURNAL OF POWER SOURCES
- State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge
- (2008) Seongjun Lee et al. JOURNAL OF POWER SOURCES
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