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Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system

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  • Shuo Li
  • Song Li
  • Haifeng Zhao
  • Yuan An

Abstract

In this article, a method for estimating the state of charge of lithium battery based on back-propagation neural network is proposed and implemented for uninterruptible power system. First, back-propagation neural network model is established with voltage, temperature, and charge–discharge current as input parameters, and state of charge of lithium battery as output parameter. Then, the back-propagation neural network is trained by Levenberg–Marquardt algorithm and gradient descent method; and the state of charge of batteries in uninterruptible power system is estimated by the trained back-propagation neural network. Finally, we build a state-of-charge estimation test platform and connect it to host computer by Ethernet. The performance of state-of-charge estimation based on back-propagation neural network is tested by connecting to uninterruptible power system and compared with the ampere-hour counting method and the actual test data. The results show that the state-of-charge estimation based on back-propagation neural network can achieve high accuracy in estimating state of charge of uninterruptible power system and can reduce the error accumulation caused in long-term operation.

Suggested Citation

  • Shuo Li & Song Li & Haifeng Zhao & Yuan An, 2019. "Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system," International Journal of Distributed Sensor Networks, , vol. 15(12), pages 15501477198, December.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:12:p:1550147719894526
    DOI: 10.1177/1550147719894526
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    References listed on IDEAS

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    1. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    2. Aamir, Muhammad & Ahmed Kalwar, Kafeel & Mekhilef, Saad, 2016. "Review: Uninterruptible Power Supply (UPS) system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1395-1410.
    3. Truchot, Cyril & Dubarry, Matthieu & Liaw, Bor Yann, 2014. "State-of-charge estimation and uncertainty for lithium-ion battery strings," Applied Energy, Elsevier, vol. 119(C), pages 218-227.
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