Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system
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 a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719894526 |
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| _version_ | 1832555281424318464 |
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| author | Shuo Li Song Li Haifeng Zhao Yuan An |
| author_facet | Shuo Li Song Li Haifeng Zhao Yuan An |
| author_sort | Shuo Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a0cbf9538b854811a7041f885b66fd3e |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-a0cbf9538b854811a7041f885b66fd3e2025-02-03T05:48:37ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-12-011510.1177/1550147719894526Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power systemShuo Li0Song Li1Haifeng Zhao2Yuan An3School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSchool of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, ChinaSchool of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, ChinaIn 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.https://doi.org/10.1177/1550147719894526 |
| spellingShingle | Shuo Li Song Li Haifeng Zhao Yuan An 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 |
| title | Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system |
| title_full | Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system |
| title_fullStr | Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system |
| title_full_unstemmed | Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system |
| title_short | Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system |
| title_sort | design and implementation of state of charge estimation based on back propagation neural network for smart uninterruptible power system |
| url | https://doi.org/10.1177/1550147719894526 |
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