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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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    4. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
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    Cited by:

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