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Real-Time Estimation of Battery State of Charge With Metabolic Grey Model and LabVIEW Platform
descriptionPublicationkeyboard_double_arrow_right Article , Journal 2018Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Authors: Dong Zheng; Huimin Wang; Jingjing An; Jing Chen; +2 Authors
Dong Zheng; Huimin Wang; Jingjing An; Jing Chen; Haihong Pan; Lin Chen;
doi: 10.1109/access.2018.2807805
doi: 10.1109/access.2018.2807805
Accurate state-of-charge (SoC) estimation is crucial to guarantee the safety and reliability of lithium-ion batteries. This paper aimed to develop an advanced battery estimation method for electric vehicles based on the grey model without the need of a high-fidelity battery model demanding high computation power. The metabolic grey model (MGM) introduced metabolism mechanism to adjust the model parameters according to the evolving operating status and conditions and estimate the state of charge. To further validate the feasibility of the proposed method, the analog acquisition, communication system, and SoC estimation algorithms were programmed to embed within a LabVIEW platform. The performance of the proposed SoC estimation with MGM algorithm was finally investigated with a battery-in-loop platform under different dynamic loading profiles. The experimental results indicated that the MGM can estimate SoC that involved small samples and poor information in real time, with the maximum errors of no over 4% under various loading conditions.
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Article . 2018 . Peer-reviewed
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Article . 2018
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Real-Time Estimation of Battery State of Charge With Metabolic Grey Model and LabVIEW Platform
descriptionPublicationkeyboard_double_arrow_right Article , Journal 2018Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Authors: Dong Zheng; Huimin Wang; Jingjing An; Jing Chen; +2 Authors
Dong Zheng; Huimin Wang; Jingjing An; Jing Chen; Haihong Pan; Lin Chen;
doi: 10.1109/access.2018.2807805
doi: 10.1109/access.2018.2807805
Accurate state-of-charge (SoC) estimation is crucial to guarantee the safety and reliability of lithium-ion batteries. This paper aimed to develop an advanced battery estimation method for electric vehicles based on the grey model without the need of a high-fidelity battery model demanding high computation power. The metabolic grey model (MGM) introduced metabolism mechanism to adjust the model parameters according to the evolving operating status and conditions and estimate the state of charge. To further validate the feasibility of the proposed method, the analog acquisition, communication system, and SoC estimation algorithms were programmed to embed within a LabVIEW platform. The performance of the proposed SoC estimation with MGM algorithm was finally investigated with a battery-in-loop platform under different dynamic loading profiles. The experimental results indicated that the MGM can estimate SoC that involved small samples and poor information in real time, with the maximum errors of no over 4% under various loading conditions.
IEEE Accessarrow_drop_down
IEEE Access
Article . 2018 . Peer-reviewed
License: IEEE Open Access
Data sources: Crossref
IEEE Access
Article
License: CC BY NC ND
Data sources: UnpayWall
IEEE Access
Article . 2018
Data sources: DOAJ
IEEE Access
Journal
Data sources: Microsoft Academic Graph
linkLink to
shareshareShare
citeCite
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Please grant OpenAIRE to access and update your ORCID works.
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15
citations 15
popularity Top 10%
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Powered by BIP!
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IEEE Accessarrow_drop_down
IEEE Access
Article . 2018 . Peer-reviewed
License: IEEE Open Access
Data sources: Crossref
IEEE Access
Article
License: CC BY NC ND
Data sources: UnpayWall
IEEE Access
Article . 2018
Data sources: DOAJ
IEEE Access
Journal
Data sources: Microsoft Academic Graph
linkLink to
shareshareShare
citeCite
addClaim
Please grant OpenAIRE to access and update your ORCID works.
This Research product is the result of merged Research products in OpenAIRE.

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Real-Time Estimation of Battery State of Charge With Metabolic Grey Model and LabVIEW Platform

Real-Time Estimation of Battery State of Charge With Metabolic Grey Model and LabVIEW Platform

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Real-Time Estimation of Battery State of Charge With Metabolic Grey Model and LabVIEW Platform

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