Fuzzy-control-based five-step Li-ion battery charger by using AC impedance technique

Houshyar Asadi; Seyed Hamidreza Aghay Kaboli; Arash Mohammadi; Maysam Oladazimi

doi:10.1117/12.920980

13 January 2012 Fuzzy-control-based five-step Li-ion battery charger by using AC impedance technique

Houshyar Asadi, Seyed Hamidreza Aghay Kaboli, Arash Mohammadi, Maysam Oladazimi

Proceedings Volume 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis; 834939 (2012) https://doi.org/10.1117/12.920980
Event: Fourth International Conference on Machine Vision (ICMV 11), 2011, Singapore, Singapore

Abstract

In This paper the previous Li-Ion battery charger techniques are reviewed and compared and the new fuzzy logic battery charging method which is proposed to optimize and improve the battery charger efficiently. According to results of comparison, using the fuzzy control charging system can shorten the charging time with higher efficiency and lower temperature rise. Additionally, we have used optimal Li-ion battery charging frequency by using AC impedance technique which means if the battery is charged by the optimal charging frequency fZmin, that obtain from Bode Plot of the Li-ion battery, the charging time and charging efficiency will improve. Thus using the switching frequency (fZmin) of the battery charger and the fuzzy logic control on the same system can optimize the performance on the charging process. According to the experimental results, the proposed charger can charge the Li-ion batteries with higher efficiency 97.16%, lower temperature rise1.513degree celosias, fast charging period around 50.43 minute and long life cycle. The results in this paper are presented by using MATLAB and dsPIC30F2020 is used as controller applying designed fuzzy logic inside.

Citation Download Citation

Houshyar Asadi, Seyed Hamidreza Aghay Kaboli, Arash Mohammadi, and Maysam Oladazimi "Fuzzy-control-based five-step Li-ion battery charger by using AC impedance technique", Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 834939 (13 January 2012); https://doi.org/10.1117/12.920980

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