State‐of‐charge estimation of lithium‐ion batteries using composite multi‐dimensional features and a neural network-Reference-Cited by-同舟云学术

State‐of‐charge estimation of lithium‐ion batteries using composite multi‐dimensional features and a neural network

Published:2019-05 Issue:6 Volume:12 Page:1470-1478
ISSN:1755-4543
Container-title:IET Power Electronics
language:en
Short-container-title:IET Power Electronics

Author:

Li Jianhua¹²³,Liu Mingsheng¹²⁴

Affiliation:

1. State Key Laboratory of Reliability and Intelligence of Electrical EquipmentHebei University of TechnologyTianjinPeople's Republic of China

2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei ProvinceHebei University of TechnologyTianjinPeople's Republic of China

3. School of Information Science and TechnologyShijiazhuang TieDao UniversityShijiazhuangPeople's Republic of China

4. Shijiazhuang Institute of Railway TechnologyShijiazhuangPeople's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-pel.2018.6144

Reference28 articles.

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3. State of charge estimation of lithium‐ionbatteries using the open‐circuit voltage at various ambient temperatures;Xing Y.J.;Appl. Energy,2014

4. State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model

5. State‐of‐charge and capacity estimation of lithium‐ion battery using a new open‐circuit voltage versus state‐of‐charge;Seongjun L.;J. Power Sources,2008

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