A 42nJ/conversion On-Demand State-of-Charge Indicator for Miniature IoT Li-ion Batteries

被引:0
|
作者
Jeong, Junwon [1 ,2 ]
Jeong, Seokhyeon [2 ]
Kim, Chulwoo [1 ]
Sylvester, Dennis [2 ]
Blaauw, David [2 ]
机构
[1] Korea Univ, Seoul, South Korea
[2] Univ Michigan, Ann Arbor, MI 48109 USA
关键词
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
An energy efficient State-of-Charge (SOC) indication algorithm and integrated system for small IoT batteries are introduced in this paper. The system is implemented in a 180-nm CMOS technology. Based on a key finding that small Li-ion batteries exhibit a linear dependence between battery voltage and load current, we propose an instantaneous linear extrapolation (ILE) algorithm and circuit allowing on-demand estimation of SOC. Power consumption is 42nW and maximum SOC indication error is 1.7%.
引用
收藏
页码:281 / 282
页数:2
相关论文
共 50 条
  • [1] A 42nJ/conversion On-Demand State-of-Charge Indicator for Miniature IoT Li-ion Batteries
    Jeong, Junwon
    Jeong, Seokhyeon
    Kim, Chulwoo
    Sylvester, Dennis
    Blaauw, David
    2017 SYMPOSIUM ON VLSI CIRCUITS, 2017, : C206 - C207
  • [2] A 42 nJ/Conversion On-Demand State-of-Charge Indicator for Miniature IoT Li-Ion Batteries
    Jeong, Junwon
    Jeong, Seokhyeon
    Sylvester, Dennis
    Blaauw, David
    Kim, Chulwoo
    IEEE JOURNAL OF SOLID-STATE CIRCUITS, 2019, 54 (02) : 524 - 537
  • [3] Ultrasonic guided waves as an indicator for the state-of-charge of Li-ion batteries
    Reichmann, Benjamin
    Sharif-Khodaei, Zahra
    JOURNAL OF POWER SOURCES, 2023, 576
  • [4] Low-Energy, Scalable, On-demand State-of-charge Estimation System for Li-ion batteries.
    Jules, Dufour
    Yvon, Savaria
    Jean-Pierre, David
    2023 21ST IEEE INTERREGIONAL NEWCAS CONFERENCE, NEWCAS, 2023,
  • [5] State-of-charge indication in Li-ion batteries by simulated impedance spectroscopy
    Brandell, Daniel (daniel.brandell@kemi.uu.se), 1600, Springer Science and Business Media B.V. (47):
  • [6] State-of-charge indication in Li-ion batteries by simulated impedance spectroscopy
    Srivastav, Shruti
    Lacey, Matthew J.
    Brandell, Daniel
    JOURNAL OF APPLIED ELECTROCHEMISTRY, 2017, 47 (02) : 229 - 236
  • [7] A Machine Learning Approach for State-of-Charge Estimation of Li-ion batteries
    Youssef, Heba Yahia
    Alkhaja, Latifa A.
    Almazrouei, Hajar Humaid
    Nassif, Ali Bou
    Ghenai, Chaouki
    AlShabi, Mohammad
    ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS APPLICATIONS IV, 2022, 12113
  • [8] State-of-charge indication in Li-ion batteries by simulated impedance spectroscopy
    Shruti Srivastav
    Matthew J. Lacey
    Daniel Brandell
    Journal of Applied Electrochemistry, 2017, 47 : 229 - 236
  • [9] State-of-Charge Estimation for Li-Ion Batteries: A More Accurate Hybrid Approach
    Misyris, George S.
    Doukas, Dimitrios I.
    Papadopoulos, Theofilos A.
    Labridis, Dimitris P.
    Agelidis, Vassilios G.
    IEEE TRANSACTIONS ON ENERGY CONVERSION, 2019, 34 (01) : 109 - 119
  • [10] On-line Parameter, State-of-Charge and Aging Estimation of Li-ion Batteries
    Rosca, B.
    Kessels, J. T. B. A.
    Bergveld, H. J.
    van den Bosch, P. P. J.
    2012 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC), 2012, : 1122 - 1127