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Lithium-ion battery SOC estimation using deep learning neural networks

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TitleInfo
Title
Lithium-ion battery SOC estimation using deep learning neural networks
Name (type = personal)
NamePart (type = family)
Wang
NamePart (type = given)
Rui
NamePart (type = date)
1996-
DisplayForm
Rui Wang
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Xi
NamePart (type = given)
Zhimin
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Zhimin Xi
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Advisory Committee
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chair
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Coit
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David
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David Coit
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Advisory Committee
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RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Guo
NamePart (type = given)
Weihong
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Weihong Guo
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
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NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
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school
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Text
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theses
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2019
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2019-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Accurate State of Charge (SOC) estimation is very important for safe and reliable use of lithium-ion battery, which is widely installed as a new energy storage device in electrical vehicles. It is one of parameters in Battery Management System (BMS) to ensure good working condition of battery in case over-charging or over-discharging problems. There are a lot of methods used in SOC estimation such as open circuit voltage, coulomb counting and Kalman filter. Kalman filter is very popular method used recent years, but the accuracy needs to be improved a lot.

Researchers start to work on predicting SOC based on neural networks. In this thesis, three different types of deep neural networks are used to estimate SOC of lithium-ion battery. they are Artificial Neural Network (ANN), Nonlinear Autoregressive Neural Network (NARX) and Recurrent Neural Network (RNN). Performances of accuracy are tested among three networks to identify ability of predicting SOC.

RNN performs best by measuring Mean Square Error (MAE) 1.21 of dataset UDDS, 3.43 of dataset NYCC and 1.51 of dataset UN. NARX has the lowest performance in estimation, but it is visual friendly in plotting trend of battery discharging process. Performance of ANN is fundamental compared with accuracy of RNN and display ability of NARX.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (authority = local)
Topic
SOC estimation
Subject (authority = LCSH)
Topic
Lithium ion batteries -- Computer simulation
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_10234
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application/pdf
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text/xml
Extent
1 online resource (viii, 45 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-bpm5-dj94
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
wang
GivenName
rui
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-11 16:22:37
AssociatedEntity
Name
rui wang
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Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2020-10-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 30th, 2020.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2019-09-25T20:44:11
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2019-09-25T20:44:11
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