Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network-Reference-Cited by-同舟云学术

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Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network

Published:2021-01 Issue: Volume:282 Page:116159
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Khaleghi Sahar,Karimi Danial,Beheshti S. Hamidreza,Hosen Md. Sazzad,Behi Hamidreza,Berecibar Maitane,Van Mierlo Joeri

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference52 articles.

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2. Lithium-ion battery aging mechanisms and life model under different charging stresses;Gao;J Power Sources,2017

3. Baghdadi I, Mathieu R, Briat O, Gyan P, Vinassa JM. Lithium-ion battery ageing assessment based on a reduced design of experiments. 2017 IEEE Veh. Power Propuls. Conf. VPPC 2017 - Proc., vol. 2018- January, Institute of Electrical and Electronics Engineers Inc.; 2018, p. 1–6. doi:10.1109/VPPC.2017.8330871.

4. Lin C, Tang A, Wang W. A Review of SOH Estimation Methods in Lithium-ion Batteries for Electric Vehicle Applications. Energy Procedia, vol. 75, Elsevier Ltd; 2015, p. 1920–5. doi:10.1016/j.egypro.2015.07.199.

5. A comprehensive review on estimation strategies used in hybrid and battery electric vehicles;Cuma;Renew Sustain Energy Rev,2015

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