Publication detail

Artificial intelligence based optimization for vibration energy harvesting applications

HADAŠ, Z. KURFÜRST, J. ONDRŮŠEK, Č. SINGULE, V.

Original Title

Artificial intelligence based optimization for vibration energy harvesting applications

English Title

Artificial intelligence based optimization for vibration energy harvesting applications

Type

journal article in Web of Science

Language

en

Original Abstract

This paper deals with optimization studies based on artificial intelligence methods. These modern optimization methods can be very useful for design improving of an electromagnetic vibration energy harvester. The vibration energy harvester is a complex mechatronic device which harvests electrical energy from ambient mechanical vibrations. The harvester design consists of a precise mechanical resonator, electromagnetic converter and electronics. The optimization study of such complex mechatronic device is complicated however artificial intelligence methods can be used for set up of optimal harvester parameters. Used optimization strategies are applied to optimize the design of the electro-magnetic vibration energy harvester according to multi-objective fitness functions. Optimization results of the harvester are summarized in this paper. Presented optimization algorithms can be used for a design of new energy harvesting systems or for improving on existing energy harvesting systems.

English abstract

This paper deals with optimization studies based on artificial intelligence methods. These modern optimization methods can be very useful for design improving of an electromagnetic vibration energy harvester. The vibration energy harvester is a complex mechatronic device which harvests electrical energy from ambient mechanical vibrations. The harvester design consists of a precise mechanical resonator, electromagnetic converter and electronics. The optimization study of such complex mechatronic device is complicated however artificial intelligence methods can be used for set up of optimal harvester parameters. Used optimization strategies are applied to optimize the design of the electro-magnetic vibration energy harvester according to multi-objective fitness functions. Optimization results of the harvester are summarized in this paper. Presented optimization algorithms can be used for a design of new energy harvesting systems or for improving on existing energy harvesting systems.

Keywords

Vibration energy harvester, energy harvesting, optimization, SOMA, artificial intelligence

RIV year

2012

Released

07.02.2012

Publisher

Springer Berlin / Heidelberg

Location

Berlín

ISBN

0946-7076

Periodical

Microsystem Technologies

Year of study

18

Number

7-8

State

DE

Pages from

1003

Pages to

1014

Pages count

12

Documents

BibTex


@article{BUT89532,
  author="Zdeněk {Hadaš} and Jiří {Kurfűrst} and Čestmír {Ondrůšek} and Vladislav {Singule}",
  title="Artificial intelligence based optimization for vibration energy harvesting applications",
  annote="This paper deals with optimization studies based on artificial intelligence methods. These modern optimization methods can be very useful for design improving of an electromagnetic vibration energy harvester. The vibration energy harvester is a complex mechatronic device which harvests electrical energy from ambient mechanical vibrations. The harvester design consists of a precise mechanical resonator, electromagnetic converter and electronics. The optimization study of such complex mechatronic device is complicated however artificial intelligence methods can be used for set up of optimal harvester parameters. Used optimization strategies are applied to optimize the design of the electro-magnetic vibration energy harvester according to multi-objective fitness functions. Optimization results of the harvester are summarized in this paper. Presented optimization algorithms can be used for a design of new energy harvesting systems or for improving on existing energy harvesting systems.",
  address="Springer Berlin / Heidelberg",
  chapter="89532",
  doi="10.1007/s00542-012-1432-1",
  howpublished="online",
  institution="Springer Berlin / Heidelberg",
  number="7-8",
  volume="18",
  year="2012",
  month="february",
  pages="1003--1014",
  publisher="Springer Berlin / Heidelberg",
  type="journal article in Web of Science"
}