State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter-Reference-Cited by-同舟云学术

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State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter

Published:2019-10 Issue: Volume:234 Page:1153-1164
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Chen Cheng,Xiong Rui,Yang Ruixin,Shen Weixiang,Sun Fengchun

Funder

National Key Research and Development Program of China

Graduate Technological Innovation Project of Beijing Institute of Technology

Advanced Energy Storage and Application (AESA) Group

Beijing Institute of Technology

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment

Reference33 articles.

1. Competition and collaboration in cooperative coevolution of elman recurrent neural networks for time-series prediction;Chandra;IEEE Trans. Neural Networks Learn. Syst.,2015

2. State of charge and state of health estimation for lithium batteries using recurrent neural networks;Chaoui;IEEE Trans. Veh. Technol.,2017

3. State-of-Charge estimation for lithium-ion batteries using neural networks and EKF;Charkhgard;IEEE Trans. Ind. Electron.,2010

4. Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries;Chemali;IEEE Trans. Ind. Electron.,2018

5. Open-circuit voltage-based state of charge estimation of lithium-ion battery using dual neural network fusion battery model;Dang;Electrochim. Acta,2016

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