The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle

Shiqiong Zhou; Longyun Kang; Guifang Guo; Yanning Zhang; Binggang Cao; Longyun Kang

doi:10.1109/WCICA.2008.4593375

The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle

Shiqiong Zhou
, Longyun Kang
, Guifang Guo
, Yanning Zhang
, Binggang Cao
, Longyun Kang

Xi'an Jiaotong University
South China University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

We investigated the optimal sizing of the energy storage system in a Solar Energy Electric Vehicle (SEEV) system. A model system was constructed for this that includes the photovoltaic power, the lead-acid battery and a flywheel.The optimal sizing can be considered as a constrained optimization problem: minimization the total capital cost of energy storage system in SEEV, subject to the main constraint of the Loss of Power Supply Probability (LPSP). The Genetic Algorithm or combinatorial optimization by Genetic Algorithm and Neural Network were used in this paper. And the decision variables are not only the capacity of batteries in traditional methods, but also the capacity of flywheel. Studies have proved that the optimization algorithms used can converge well and they are feasible. Combinatorial optimization by Genetic Algorithm and Neural Network can lessen the calculation time, with the results change little.

Original language	English
Title of host publication	Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08
Pages	2838-2842
Number of pages	5
DOIs	https://doi.org/10.1109/WCICA.2008.4593375
State	Published - 2008
Externally published	Yes
Event	7th World Congress on Intelligent Control and Automation, WCICA'08 - Chongqing, China Duration: 25 Jun 2008 → 27 Jun 2008

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

Conference

Conference	7th World Congress on Intelligent Control and Automation, WCICA'08
Country/Territory	China
City	Chongqing
Period	25/06/08 → 27/06/08

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Combinatorial optimization
Genetic algorithm
Loss of power supply probability
Neural network

Access to Document

10.1109/WCICA.2008.4593375

Cite this

Zhou, S., Kang, L., Guo, G., Zhang, Y., Cao, B., & Kang, L. (2008). The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle. In Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08 (pp. 2838-2842). Article 4593375 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA)). https://doi.org/10.1109/WCICA.2008.4593375

Zhou, Shiqiong ; Kang, Longyun ; Guo, Guifang et al. / The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle. Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08. 2008. pp. 2838-2842 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA)).

@inproceedings{e4b548503a484f3f9a864f841277fced,

title = "The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle",

abstract = "We investigated the optimal sizing of the energy storage system in a Solar Energy Electric Vehicle (SEEV) system. A model system was constructed for this that includes the photovoltaic power, the lead-acid battery and a flywheel.The optimal sizing can be considered as a constrained optimization problem: minimization the total capital cost of energy storage system in SEEV, subject to the main constraint of the Loss of Power Supply Probability (LPSP). The Genetic Algorithm or combinatorial optimization by Genetic Algorithm and Neural Network were used in this paper. And the decision variables are not only the capacity of batteries in traditional methods, but also the capacity of flywheel. Studies have proved that the optimization algorithms used can converge well and they are feasible. Combinatorial optimization by Genetic Algorithm and Neural Network can lessen the calculation time, with the results change little.",

keywords = "Combinatorial optimization, Genetic algorithm, Loss of power supply probability, Neural network",

author = "Shiqiong Zhou and Longyun Kang and Guifang Guo and Yanning Zhang and Binggang Cao and Longyun Kang",

year = "2008",

doi = "10.1109/WCICA.2008.4593375",

language = "英语",

isbn = "9781424421145",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

pages = "2838--2842",

booktitle = "Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08",

note = "7th World Congress on Intelligent Control and Automation, WCICA'08 ; Conference date: 25-06-2008 Through 27-06-2008",

}

Zhou, S, Kang, L, Guo, G, Zhang, Y, Cao, B & Kang, L 2008, The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle. in Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08., 4593375, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), pp. 2838-2842, 7th World Congress on Intelligent Control and Automation, WCICA'08, Chongqing, China, 25/06/08. https://doi.org/10.1109/WCICA.2008.4593375

The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle. / Zhou, Shiqiong; Kang, Longyun; Guo, Guifang et al.
Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08. 2008. p. 2838-2842 4593375 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle

AU - Zhou, Shiqiong

AU - Kang, Longyun

AU - Guo, Guifang

AU - Zhang, Yanning

AU - Cao, Binggang

AU - Kang, Longyun

PY - 2008

Y1 - 2008

N2 - We investigated the optimal sizing of the energy storage system in a Solar Energy Electric Vehicle (SEEV) system. A model system was constructed for this that includes the photovoltaic power, the lead-acid battery and a flywheel.The optimal sizing can be considered as a constrained optimization problem: minimization the total capital cost of energy storage system in SEEV, subject to the main constraint of the Loss of Power Supply Probability (LPSP). The Genetic Algorithm or combinatorial optimization by Genetic Algorithm and Neural Network were used in this paper. And the decision variables are not only the capacity of batteries in traditional methods, but also the capacity of flywheel. Studies have proved that the optimization algorithms used can converge well and they are feasible. Combinatorial optimization by Genetic Algorithm and Neural Network can lessen the calculation time, with the results change little.

AB - We investigated the optimal sizing of the energy storage system in a Solar Energy Electric Vehicle (SEEV) system. A model system was constructed for this that includes the photovoltaic power, the lead-acid battery and a flywheel.The optimal sizing can be considered as a constrained optimization problem: minimization the total capital cost of energy storage system in SEEV, subject to the main constraint of the Loss of Power Supply Probability (LPSP). The Genetic Algorithm or combinatorial optimization by Genetic Algorithm and Neural Network were used in this paper. And the decision variables are not only the capacity of batteries in traditional methods, but also the capacity of flywheel. Studies have proved that the optimization algorithms used can converge well and they are feasible. Combinatorial optimization by Genetic Algorithm and Neural Network can lessen the calculation time, with the results change little.

KW - Combinatorial optimization

KW - Genetic algorithm

KW - Loss of power supply probability

KW - Neural network

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ER -

Zhou S, Kang L, Guo G, Zhang Y, Cao B, Kang L. The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle. In Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08. 2008. p. 2838-2842. 4593375. (Proceedings of the World Congress on Intelligent Control and Automation (WCICA)). doi: 10.1109/WCICA.2008.4593375

The combinatorial optimization by genetic algorithm and neural network for energy storage system in solar energy electric vehicle

Abstract

Publication series

Conference

UN SDGs

Keywords

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