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    Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107085


    Title: DSP-based probabilistic fuzzy neural network control for li-ion battery charger
    Authors: 林法正;Lin, Faa-Jeng;Huang, Ming-Shi;Yeh, Po-Yi;Tsai, Han-Chang;Kuan, Chi-Hsuan
    Contributors: 資訊電機學院電機工程學系
    Keywords: AC-DC power converters;Algorithms;Applied sciences;Batteries;Constant-current (CC) charging;constant-voltage (CV) charging DSP;Control systems;Controllers;Electric currents;Electric, optical and optoelectronic circuits;Electrical engineering. Electrical power engineering;Electrical machines;Electronics;Exact sciences and technology;Fuzzy control;Fuzzy neural networks;Integrated circuits;Integrated circuits by function (including memories and processors);Miscellaneous;Neural networks;phase-shift full-bridge (PSFB);power factor correction (PFC);probabilistic fuzzy neural network (PFNN);Probabilistic logic;Regulation and control;Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices;Various equipment and components;Voltage control
    Date: 2012-05-02
    Issue Date: 2026-04-23 13:55:49 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers Inc.;New York, NY: IEEE
    Abstract: 摘要: A DSP-based probabilistic fuzzy neural network (PFNN) controller to control a two-stage ac-dc charger is pro- posed in this study. The charger is composed of an ac-dc boost converter with power factor correction and a phase-shift full- bridge dc-dc converter. Moreover, the designed charger adopts a constant-current and constant-voltage (CC-CV) charging strategy to charge lithium-ion battery packs. To improve the transient of voltage regulation during load variation, a PFNN controller is pro- posed to replace the traditional proportional-integral controller. Furthermore, the discontinuous charging voltage and current during the transition between the CC and CV charging modes can also be reduced significantly using the proposed PFNN controller. The network structure and the online learning algorithms of the PFNN controller are introduced in detail. In addition, the control performances of the proposed PFNN control system for CC-CV charging are evaluated by experimental results.
    其他題名: TPEL
    出版者: New York, NY: IEEE
    出版日期: 2012-08-01
    出處: IEEE Transactions on Power Electronics, 2012-08, Vol.27 (8), p.3782-3794
    資源來源: IEEE Electronic Library (IEL)
    版權: 2015 INIST-CNRS
    版權: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Aug 2012
    識別號: ISSN: 0885-8993
    識別號: EISSN: 1941-0107
    識別號: DOI: 10.1109/TPEL.2012.2187073
    識別號: CODEN: ITPEE8
    Appears in Collections:[Department of Electrical Engineering] journal & Dissertation

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