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State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods

Received: 28 June 2018     Accepted: 9 July 2018     Published: 26 July 2018
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Abstract

The state of charge (SOC) estimation plays important role in the battery energy storage system (BESS). Nowadays many semiconductor companies are paying more and more attention and investment to support many researchers to implement the state of charge for the batteries storage. the key to optimize the batteries storage is determine SOC value based on accuracy methods. a number of brief methods for SOC determination have been studied and compared with traditional methods the adaptive methods shown precise result because didn’t consider the dynamic effect of the batteries. In this paper, we use combination methods to estimate the SOC for lead-acid battery storage under two charge techniques namely Maximum Power Point Tracking – Plus Width Module (MPPT- PWM) when considering the effect of voltage drops on the estimation of SOC. The model uses the coulomb counting as an algorithm to determine the SOC and set it as a target in the backpropagation function in artificial neural network in MATLAB program (R2016a 64-bit (win64)). The simulation results show that the model is very precise to estimate the SOC in realistic operation.

Published in International Journal of Systems Engineering (Volume 2, Issue 1)
DOI 10.11648/j.ijse.20180201.11
Page(s) 1-8
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

State of Charge (SOC), Lead-Acid Battery, Coulomb Counting (AH), Backpropagation (PB)

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  • APA Style

    Waleed Karrar, Zhen Zhang. (2018). State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods. International Journal of Systems Engineering, 2(1), 1-8. https://doi.org/10.11648/j.ijse.20180201.11

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    ACS Style

    Waleed Karrar; Zhen Zhang. State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods. Int. J. Syst. Eng. 2018, 2(1), 1-8. doi: 10.11648/j.ijse.20180201.11

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    AMA Style

    Waleed Karrar, Zhen Zhang. State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods. Int J Syst Eng. 2018;2(1):1-8. doi: 10.11648/j.ijse.20180201.11

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  • @article{10.11648/j.ijse.20180201.11,
      author = {Waleed Karrar and Zhen Zhang},
      title = {State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods},
      journal = {International Journal of Systems Engineering},
      volume = {2},
      number = {1},
      pages = {1-8},
      doi = {10.11648/j.ijse.20180201.11},
      url = {https://doi.org/10.11648/j.ijse.20180201.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijse.20180201.11},
      abstract = {The state of charge (SOC) estimation plays important role in the battery energy storage system (BESS). Nowadays many semiconductor companies are paying more and more attention and investment to support many researchers to implement the state of charge for the batteries storage. the key to optimize the batteries storage is determine SOC value based on accuracy methods. a number of brief methods for SOC determination have been studied and compared with traditional methods the adaptive methods shown precise result because didn’t consider the dynamic effect of the batteries. In this paper, we use combination methods to estimate the SOC for lead-acid battery storage under two charge techniques namely Maximum Power Point Tracking – Plus Width Module (MPPT- PWM) when considering the effect of voltage drops on the estimation of SOC. The model uses the coulomb counting as an algorithm to determine the SOC and set it as a target in the backpropagation function in artificial neural network in MATLAB program (R2016a 64-bit (win64)). The simulation results show that the model is very precise to estimate the SOC in realistic operation.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods
    AU  - Waleed Karrar
    AU  - Zhen Zhang
    Y1  - 2018/07/26
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijse.20180201.11
    DO  - 10.11648/j.ijse.20180201.11
    T2  - International Journal of Systems Engineering
    JF  - International Journal of Systems Engineering
    JO  - International Journal of Systems Engineering
    SP  - 1
    EP  - 8
    PB  - Science Publishing Group
    SN  - 2640-4230
    UR  - https://doi.org/10.11648/j.ijse.20180201.11
    AB  - The state of charge (SOC) estimation plays important role in the battery energy storage system (BESS). Nowadays many semiconductor companies are paying more and more attention and investment to support many researchers to implement the state of charge for the batteries storage. the key to optimize the batteries storage is determine SOC value based on accuracy methods. a number of brief methods for SOC determination have been studied and compared with traditional methods the adaptive methods shown precise result because didn’t consider the dynamic effect of the batteries. In this paper, we use combination methods to estimate the SOC for lead-acid battery storage under two charge techniques namely Maximum Power Point Tracking – Plus Width Module (MPPT- PWM) when considering the effect of voltage drops on the estimation of SOC. The model uses the coulomb counting as an algorithm to determine the SOC and set it as a target in the backpropagation function in artificial neural network in MATLAB program (R2016a 64-bit (win64)). The simulation results show that the model is very precise to estimate the SOC in realistic operation.
    VL  - 2
    IS  - 1
    ER  - 

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Author Information
  • College of Mechanical and Electrical Engineering, Hohai University, Changzhou, China

  • College of Mechanical and Electrical Engineering, Hohai University, Changzhou, China

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