Forecasting the state-of-charge of Li-ion batteries using fuzzy inference system and fuzzy identification

Ho Ta Lin, Tsorng-Juu Liang, Shih Ming Chen, Kuan Wen Li

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

5 Citations (Scopus)

Abstract

This study proposes a method to forecast the state of charge (SOC) of Li-ion batteries using Fuzzy inference system and Fuzzy identification. In this study, 5 pieces of Li-Co batteries were used in this research for the life-cycle testing. The cycle testing includes CC (0.5C)/CV (4.2V) charge, CC (0.2, 0.4, 0.6, 0.8, 1C) discharge, and the rest time (one minute). The life-cycle testing indicates the relations of the voltage, the discharging time and the SOC with various life-cycles and various discharging currents. This study forecast the SOC with the data of the above, Fuzzy inference system and Fuzzy identification. This study also compares the SOC forecast accuracy using Fuzzy inference system, Fuzzy identification, and Fuzzy inference system combined with Fuzzy identification. The testing results reveal that the average error, the standard deviation, the maximum error, and the minimum error of the forecasted SOC was 0.4%, 6%, 18% and 25.1%, respectively. The 81.48% of the forecasted SOC error is within ± 5%.

Original languageEnglish
Title of host publication2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012
Pages3175-3181
Number of pages7
DOIs
Publication statusPublished - 2012 Dec 17
Event4th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2012 - Raleigh, NC, United States
Duration: 2012 Sep 152012 Sep 20

Publication series

Name2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012

Other

Other4th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2012
CountryUnited States
CityRaleigh, NC
Period12-09-1512-09-20

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Fuel Technology

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    Lin, H. T., Liang, T-J., Chen, S. M., & Li, K. W. (2012). Forecasting the state-of-charge of Li-ion batteries using fuzzy inference system and fuzzy identification. In 2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012 (pp. 3175-3181). [6342349] (2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012). https://doi.org/10.1109/ECCE.2012.6342349