State of charge estimation based on adaptive neuro-fuzzy inference system

被引：0
|
作者：
Guan Jiansheng [1 ]
Xu Wenjin [1 ]
Zhang Abu [1 ]
机构：
[1] Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
来源：
ICCSE'2006: Proceedings of the First International Conference on Computer Science & Education: ADVANCED COMPUTER TECHNOLOGY, NEW EDUCATION | 2006年
关键词：
state of charge (SOC); Adaptive Neuro-Fuzzy Inference System (ANFIS); battery;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we describe a method to estimate state of charge using an adaptive neuro-fuzzy inference system (ANFIS). Using a given input/output battery data set we obtain a fuzzy inference system (FIS) whose membership function parameters are tuned using an optimization algorithm. This allows fuzzy system to learn from the data he is modelling. That is, we use ANFIS to train a FIS model to emulate the data presented to it by modifying the membership function parameters according to a chosen error criterion. Input variables include the AC resistance, the DC internal resistance and the load voltage in battery management system. SOC are the output.
引用
收藏
页码：840 / 843
页数：4
相关论文
共 50 条
  • [1] Channel estimation based on adaptive neuro-fuzzy inference system in OFDM
    Seyman, M. Nuri
    Taspinar, Necmi
    [J]. IEICE TRANSACTIONS ON COMMUNICATIONS, 2008, E91B (07) : 2426 - 2430
  • [2] Battery state-of-charge (SOC) estimation using adaptive neuro-fuzzy inference system (ANFIS)
    Cai, CH
    Du, D
    Liu, ZY
    [J]. PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2, 2003, : 1068 - 1073
  • [3] Adaptive Neuro-fuzzy Inference system into Induction Motor : Estimation
    Boussada, Zina
    Ben Hamed, Mouna
    Sbita, Lassaad
    [J]. 2014 INTERNATIONAL CONFERENCE ON ELECTRICAL SCIENCES AND TECHNOLOGIES IN MAGHREB (CISTEM), 2014,
  • [4] Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
    Luo, Yunhui
    Wang, Xingguang
    Wang, Qing
    Chen, Yehong
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (21):
  • [5] Fuzzy nonparametric regression based on an adaptive neuro-fuzzy inference system
    Danesh, Sedigheh
    Farnoosh, Rahman
    Razzaghnia, Tahereh
    [J]. NEUROCOMPUTING, 2016, 173 : 1450 - 1460
  • [6] Design of Adaptive Neuro-Fuzzy Inference System for Estimation of Grinding Performance
    Baseri, H.
    [J]. MATERIALS AND MANUFACTURING PROCESSES, 2011, 26 (05) : 757 - 763
  • [7] Runoff estimation using modified adaptive neuro-fuzzy inference system
    Nath, Amitabha
    Mthethwa, Fisokuhle
    Saha, Goutam
    [J]. ENVIRONMENTAL ENGINEERING RESEARCH, 2020, 25 (04) : 545 - 553
  • [8] Swine live weight estimation by adaptive neuro-fuzzy inference system
    Okinda, Cedric
    Liu, Longhen
    Zhang, Guangyue
    Shen, Mingxia
    [J]. INDIAN JOURNAL OF ANIMAL RESEARCH, 2018, 52 (06) : 923 - 928
  • [9] Unsaturated soils permeability estimation by adaptive neuro-fuzzy inference system
    Jokar, Mehdi Hashemi
    Khosravi, Abdolkarim
    Heidaripanah, Ali
    Soltani, Fazlollah
    [J]. SOFT COMPUTING, 2019, 23 (16) : 6871 - 6881
  • [10] Unsaturated soils permeability estimation by adaptive neuro-fuzzy inference system
    Mehdi Hashemi Jokar
    Abdolkarim Khosravi
    Ali Heidaripanah
    Fazlollah Soltani
    [J]. Soft Computing, 2019, 23 : 6871 - 6881
12345