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INDONESIA
Mechatronics, Electrical Power, and Vehicular Technology
Published by Lembaga Ilmu Pengetahuan Indonesia
ISSN : 20873379     EISSN : 20886985     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Mechatronics, Electrical Power, and Vehicular Technology (hence MEV) is a journal aims to be a leading peer-reviewed platform and an authoritative source of information. We publish original research papers, review articles and case studies focused on mechatronics, electrical power, and vehicular technology as well as related topics. All papers are peer-reviewed by at least two referees. MEV is published and imprinted by Research Center for Electrical Power and Mechatronics - Indonesian Institute of Sciences and managed to be issued twice in every volume. For every edition, the online edition is published earlier than the print edition.
Arjuna Subject : -
Articles 22 Documents
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Search results for , issue "Vol 6, No 1 (2015)" : 22 Documents clear
Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle Bambang Wahono; Kristian Ismail; Harutoshi Ogai
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 1 (2015)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2015.v6.31-38

Abstract

This paper presents the construction of a battery state of charge (SOC) prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO) succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.
Appendix MEV Vol 6 Iss 1 Ghalya Pikra
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 1 (2015)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2015.v6.%p

Abstract

Page 3 of 3 | Total Record : 22

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