State-of-Charge Estimation of VRLA Batteries Using Neural Networks and Extended Kalman Filter

Author:

Shahriari Mehrnoosh,Farrokhi Mohammad

Publisher

Elsevier BV

Subject

General Medicine

Reference27 articles.

1. VRLA battery state-of-charge estimation in telecommunication power systems;Anbuky;IEEE Transactions on Industrial Electronics,2000

2. Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles;Bhangu;IEEE Transactions on Vehicular Technology,2005

3. An electrochemical technique for state of charge (SOC) probing of positive lead-acid battery plate;Blood;Journal of Power Sources,2002

4. Cal, C.H., Du, D., Liu Z.Y., and Zhang, H. (2002). Artificial neural network in estimation of battery state-of-charge(SOC) with nonconventional input variables selected by correlation analysis. Conference on Machine Learning and Cybernetics, 1619-1625, Beijing, China.

5. Design of an optimal battery pulse charge system by frequency-varied technique;Chen;IEEE Transactions on Industrial Electronics,2007

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Simulation Study on Battery State of Charge Estimation Using Kalman Filter;Journal of Advanced Computational Intelligence and Intelligent Informatics;2016-11-20

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