FPGA Implementation of the Mix Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

被引:0
|
作者
Baronti, Federico [1 ]
Roncella, Roberto [1 ]
Saletti, Roberto [1 ]
Zamboni, Walter [2 ]
机构
[1] Univ Pisa, Dip Ingn Informaz, I-56100 Pisa, Italy
[2] Univ Salerno, Dip Ingn Informaz Ingn Elettr & Matemat Applicata, Fisciano Sa, Italy
关键词
MANAGEMENT;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper describes the hardware implementation of a model-based State-of-Charge (SoC) estimation algorithm for Lithium-ion batteries. SoC estimation is essential to evaluate the remaining runtime of the battery, as well as to enhance its safety and life expectancy. Model-based SoC estimation is a good solution to the problem, but only offline tests have been presented so far. In this work, the SoC estimation algorithm is implemented on an FPGA device, following an innovative and automatic development flow, which starts from a MATLAB/Simulink model of the algorithm. The SoC estimation hardware block is combined with a soft-core processor to form a System on a Programmable Chip. Experimental results obtained exerting the battery with a current profile that simulates its operation in an electric vehicle are presented and discussed.
引用
收藏
页码:5641 / 5646
页数:6
相关论文
共 50 条
  • [1] An Online Estimation Algorithm of State-of-Charge of Lithium-ion Batteries
    Feng, Yong
    Meng, Cheng
    Han, Fengling
    Yi, Xun
    Yu, Xinghuo
    [J]. IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2018, : 3879 - 3882
  • [2] Implementation of State-of-Charge and State-of-Health Estimation for Lithium-Ion Batteries
    Lin, Chang-Hua
    Wang, Chien-Ming
    Ho, Chien-Yeh
    [J]. PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2016, : 4790 - 4795
  • [3] On state-of-charge determination for lithium-ion batteries
    Li, Zhe
    Huang, Jun
    Liaw, Bor Yann
    Zhang, Jianbo
    [J]. JOURNAL OF POWER SOURCES, 2017, 348 : 281 - 301
  • [4] Adaptive Parameter Identification and State-of-Charge Estimation of Lithium-Ion Batteries
    Rahimi-Eichi, Habiballah
    Chow, Mo-Yuen
    [J]. 38TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2012), 2012, : 4012 - 4017
  • [5] State-of-charge estimation of lithium-ion batteries using LSTM and UKF
    Yang, Fangfang
    Zhang, Shaohui
    Li, Weihua
    Miao, Qiang
    [J]. ENERGY, 2020, 201
  • [6] An improved adaptive estimator for state-of-charge estimation of lithium-ion batteries
    Zhang, Wenjie
    Wang, Liye
    Wang, Lifang
    Liao, Chenglin
    [J]. JOURNAL OF POWER SOURCES, 2018, 402 : 422 - 433
  • [7] Nonlinear Observer Designs for State-of-Charge Estimation of Lithium-ion Batteries
    Dey, Satadru
    Ayalew, Beshah
    [J]. 2014 AMERICAN CONTROL CONFERENCE (ACC), 2014, : 248 - 253
  • [8] State-of-charge estimation in lithium-ion batteries: A particle filter approach
    Tulsyan, Aditya
    Tsai, Yiting
    Gopaluni, R. Bhushan
    Braatz, Richard D.
    [J]. JOURNAL OF POWER SOURCES, 2016, 331 : 208 - 223
  • [9] Comparison of State-of-Charge Estimation Methods for Stationary Lithium-Ion Batteries
    Berrueta, A.
    San Martin, I.
    Sanchis, P.
    Ursua, A.
    [J]. PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2016, : 2010 - 2015
  • [10] State-of-charge estimation of lithium-ion batteries based on ultrasonic detection
    Cai, Zhiduan
    Pan, Tianle
    Jiang, Haoye
    Li, Zuxin
    Wang, Yulong
    [J]. JOURNAL OF ENERGY STORAGE, 2023, 65