State-of-charge estimation for battery management system using optimized support vector machine for regression-Reference-Cited by-同舟云学术

State-of-charge estimation for battery management system using optimized support vector machine for regression

Published:2014-12 Issue: Volume:269 Page:682-693
ISSN:0378-7753
Container-title:Journal of Power Sources
language:en
Short-container-title:Journal of Power Sources

Author:

Hu J.N.,Hu J.J.,Lin H.B.,Li X.P.,Jiang C.L.,Qiu X.H.,Li W.S.

Funder

National Natural Science Foundation of China and Natural Science Foundation of Guangdong Province

National Natural Science Foundation

Natural Science Fund of Guangdong Province

Science and Technology in Guangdong Province

Department of Education of Guangdong Province

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Physical and Theoretical Chemistry,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference27 articles.

1. Prediction of state-of-charge effects on lead-acid battery characteristics using neural network parameter modifier

2. Acetylene black-embedded LiMn0.8Fe0.2PO4/C composite as cathode for lithium ion battery

3. Preparation of hierarchical porous carbon and its rate performance as anode of lithium ion battery

4. State of charge (SOC) dependence of lithium carbonate on LiNi0.8Co0.15Al0.05O2 electrode for lithium-ion batteries

5. Equivalent circuit model parameters of a high-power Li-ion battery: Thermal and state of charge effects

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