A GA optimization for lithium–ion battery equalization based on SOC estimation by NN and FLC
•Dynamic SOC is estimated on basis of Ah–EMF by FLC–NN algorithm.•Equalization is optimized by GA to improve time efficiency and energy efficiency.•Two-stage DC/DC converter architecture is developed for equalizer design. An intelligent control proposal for battery equalization is presented by genet...
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| Vydáno v: | International journal of electrical power & energy systems Ročník 73; s. 318 - 328 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.12.2015
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| ISSN: | 0142-0615, 1879-3517 |
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| Abstract | •Dynamic SOC is estimated on basis of Ah–EMF by FLC–NN algorithm.•Equalization is optimized by GA to improve time efficiency and energy efficiency.•Two-stage DC/DC converter architecture is developed for equalizer design.
An intelligent control proposal for battery equalization is presented by genetic algorithm optimization integrated with fuzzy logic control–neural network algorithm. First, an effective two-stage DC/DC converter architecture is developed, which pave the way for the hardware module. Then, an equivalent circuit model in weighted combination with ampere-hour counting method is adopted by fuzzy logic control scheme to obtain static SOC estimation. Then the dynamic battery SOC is precisely estimated on basis of static SOC by means of neural network. The most important is the genetic algorithm optimization for battery equalization to improve the energy efficiency and time efficiency of the equalization system. Finally, certification of simulation is demonstrated to validate the proposed novel equalization scheme. |
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| AbstractList | •Dynamic SOC is estimated on basis of Ah–EMF by FLC–NN algorithm.•Equalization is optimized by GA to improve time efficiency and energy efficiency.•Two-stage DC/DC converter architecture is developed for equalizer design.
An intelligent control proposal for battery equalization is presented by genetic algorithm optimization integrated with fuzzy logic control–neural network algorithm. First, an effective two-stage DC/DC converter architecture is developed, which pave the way for the hardware module. Then, an equivalent circuit model in weighted combination with ampere-hour counting method is adopted by fuzzy logic control scheme to obtain static SOC estimation. Then the dynamic battery SOC is precisely estimated on basis of static SOC by means of neural network. The most important is the genetic algorithm optimization for battery equalization to improve the energy efficiency and time efficiency of the equalization system. Finally, certification of simulation is demonstrated to validate the proposed novel equalization scheme. |
| Author | Zhao, XiaoWei Yang, Lin Qiang, JiaXi Zhang, ShuMei |
| Author_xml | – sequence: 1 givenname: ShuMei surname: Zhang fullname: Zhang, ShuMei – sequence: 2 givenname: Lin surname: Yang fullname: Yang, Lin email: lmeimeib@hotmail.com – sequence: 3 givenname: XiaoWei surname: Zhao fullname: Zhao, XiaoWei – sequence: 4 givenname: JiaXi surname: Qiang fullname: Qiang, JiaXi |
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| SubjectTerms | Ampere-hour counting method Equivalent circuit model Fuzzy logic control–neural network algorithm Genetic algorithm optimization Two-stage DC/DC converter |
| Title | A GA optimization for lithium–ion battery equalization based on SOC estimation by NN and FLC |
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