A novel multi-model probability based battery state-of-charge fusion estimation approach

被引：4
|
作者：
Mu, Hao [1 ]
Xiong, Rui [1 ]
Sun, Fengchun [1 ]
机构：
[1] Beijing Inst Technol, Sch Mech Engn, Collaborat Innovat Ctr Elect Vehicles Beijing, 5 South Zhongguancun St, Beijing 100081, Peoples R China
来源：
CUE 2015 - APPLIED ENERGY SYMPOSIUM AND SUMMIT 2015: LOW CARBON CITIES AND URBAN ENERGY SYSTEMS | 2016年 / 88卷
关键词：
Electric vehicles; batteries; state estimation; multi-model probability; H infinity;
D O I：
10.1016/j.egypro.2016.06.061
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Accurate state of charge (SoC) estimation is very important for managing battery with safety and efficiency. In order to improve the reliability and redundancy of the SoC estimation, the multi-model probability fusion estimation (MMPFE) method is presented. Considering that the estimation results being dependent on models, the MMPFE method is utilized to fuse the SoC results gained by different equivalent circuit models (ECMs). LFP type battery are tested to verify the effectiveness of the method. Results indicate that the proposed approach can achieve accurate battery SoC estimation with good robust and reliability. (C) 2016 The Authors. Published by Elsevier Ltd.
引用
收藏
页码：840 / 846
页数：7
相关论文
共 50 条
  • [41] Online Battery State-of-Charge Estimation Based on Sparse Gaussian Process Regression
    Ozcan, Gozde
    Pajovic, Milutin
    Sahinoglu, Zafer
    Wang, Yebin
    Orlik, Philip V.
    Wada, Toshihiro
    [J]. 2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM), 2016,
  • [42] Battery State-of-charge Estimation based on H∞ Filter for Hybrid Electric Vehicle
    Yan, Jingyu
    Xu, Guoqing
    Xu, Yangsheng
    Xie, Benliang
    [J]. 2008 10TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION: ICARV 2008, VOLS 1-4, 2008, : 464 - +
  • [43] Time Scale-Based Observer Design for Battery State-of-Charge Estimation
    Gravagne, Ian
    Davis, John M.
    Poulsen, Dylan
    [J]. 2012 44TH SOUTHEASTERN SYMPOSIUM ON SYSTEM THEORY (SSST), 2012, : 12 - 17
  • [44] Battery state-of-charge estimation based on chaos immune evolutionary neural network
    Cheng, Bo
    Han, Lin
    Guo, Zhen-Yu
    Wang, Jun-Ping
    Cao, Bing-Gang
    [J]. Xitong Fangzhen Xuebao / Journal of System Simulation, 2008, 20 (11): : 2889 - 2892
  • [45] LMI-based Robust Observer Design for Battery State-of-Charge Estimation
    Dreef, H. J.
    Beelen, H. P. G. J.
    Donkers, M. C. F.
    [J]. 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, : 5716 - 5721
  • [46] State-of-charge estimation for lithium-ion battery based on PNGV model and particle filter algorithm
    Yuanfei Geng
    Hui Pang
    Xiaofei Liu
    [J]. Journal of Power Electronics, 2022, 22 : 1154 - 1164
  • [47] State-of-charge estimation for lithium-ion battery based on PNGV model and particle filter algorithm
    Geng, Yuanfei
    Pang, Hui
    Liu, Xiaofei
    [J]. JOURNAL OF POWER ELECTRONICS, 2022, 22 (07) : 1154 - 1164
  • [48] Co-estimation of state-of-charge and capacity for series-connected battery packs based on multi-method fusion and field data
    Liu, Wenxue
    Che, Yunhong
    Han, Jie
    Deng, Zhongwei
    Hu, Xiaosong
    Song, Ziyou
    [J]. JOURNAL OF POWER SOURCES, 2024, 615
  • [49] A framework for battery internal temperature and state-of-charge estimation based on fractional-order thermoelectric model
    Wang, Yujie
    Zhou, Caijie
    Zhao, Guanghui
    Chen, Zonghai
    [J]. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2022,
  • [50] MODEL-BASED STATE-OF-CHARGE ESTIMATION FOR A VALVE-REGULATED LEAD-ACID BATTERY
    Shen, Zheng
    Rahn, Christopher D.
    [J]. PROCEEDINGS OF THE ASME 5TH ANNUAL DYNAMIC SYSTEMS AND CONTROL DIVISION CONFERENCE AND JSME 11TH MOTION AND VIBRATION CONFERENCE, DSCC 2012, VOL 1, 2013, : 279 - +
12345