Fuzzy neural network genetic approach to design the SOC estimator for battery powered electric scooter

Yuang Shung Lee, Tsung Yuan Kuo, Wei-Yen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

This paper presents a new method for estimating the individual battery state-of-charge (SOC) of electric scooter (ES). The proposed method is to model ES batteries by using the fuzzy inference neural network system. A reduced form genetic algorithm (RGA) is employed to tune control point of the B-spline membership functions (BMFs) and the weightings of the fuzzy neural network (FNN). The proposed FNN with RGA (FNNRGA) optimization approach can achieve the faster learning rate and lower estimating error than the conventional gradient descent method. The validity of the SOC estimator is further verified by a constructed multiple input multiple output (MIMO) FNN structure for estimating the SOCs of battery powered ES. A fixed velocity discharging profiles of the ES batteries are investigated to train the FNN for precise estimating the SOCs of the battery strings. Furthermore, a testing data profile is used to demonstrate the superior robust and over-fitting suppressed performance of the proposed method. The estimated SOCs are directly compared with the actual SOCs under different FNN methods, verifying the accuracy and the effectiveness of the proposed intelligent modeling method.

Original languageEnglish
Title of host publication2004 IEEE 35th Annual Power Electronics Specialists Conference, PESC04
Pages2759-2765
Number of pages7
DOIs
Publication statusPublished - 2004 Nov 29
Event2004 IEEE 35th Annual Power Electronics Specialists Conference, PESC04 - Aachen, Germany
Duration: 2004 Jun 202004 Jun 25

Publication series

NamePESC Record - IEEE Annual Power Electronics Specialists Conference
Volume4
ISSN (Print)0275-9306

Other

Other2004 IEEE 35th Annual Power Electronics Specialists Conference, PESC04
CountryGermany
CityAachen
Period04/6/2004/6/25

ASJC Scopus subject areas

  • Modelling and Simulation
  • Condensed Matter Physics
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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  • Cite this

    Lee, Y. S., Kuo, T. Y., & Wang, W-Y. (2004). Fuzzy neural network genetic approach to design the SOC estimator for battery powered electric scooter. In 2004 IEEE 35th Annual Power Electronics Specialists Conference, PESC04 (pp. 2759-2765). (PESC Record - IEEE Annual Power Electronics Specialists Conference; Vol. 4). https://doi.org/10.1109/PESC.2004.1355270