NDLI: The combinatorial optimization by Genetic Algorithm and Neural Network for energy storage system in Solar Energy Electric Vehicle

Content Provider	IEEE Xplore Digital Library
Author	Shiqiong Zhou Longyun Kang Guifang Guo Yanning Zhang Binggang Cao
Copyright Year	2008
Description	Author affiliation: Sch. of Mech. Eng., Univ. of Xian Jiaotong, Xian (Shiqiong Zhou; Longyun Kang; Guifang Guo; Yanning Zhang; Binggang Cao)
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.
Starting Page	2838
Ending Page	2842
File Size	143982
Page Count	5
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4593375
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Artificial neural networks Gallium Genetic algorithms Batteries Flywheels Energy storage Loss of Power Supply Probability Genetic Algorithm Combinatorial Optimization Neural Network
Content Type	Text
Resource Type	Article

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