A comprehensive model for battery State of Charge prediction

被引:0
|
作者
Homan, Bart [1 ]
Smit, Gerard J. M. [1 ]
van Leeuwen, Richard P. [2 ]
ten Kortenaar, Marnix V. [3 ]
机构
[1] Univ Twente, Comp Architectures & Embedded Syst, Enschede, Netherlands
[2] Saxion Univ Appl Sci, Chair Renewable Energy, Enschede, Netherlands
[3] Dr Ten BV, Wezep, Netherlands
关键词
Storage; Predictive model; Smart grid; Energy management;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper the relatively simple model for State of Charge prediction, based on energy conservation, introduced in [1] is improved and verified. The model as introduced in [1] is verified for Pb-acid, Li-ion and Seasalt batteries. The model is further improved to accommodate the rate capacity effect and the capacity recovery effect, the improvements are verified with lead-acid batteries. For further verification the model is applied on a realistic situation and compared to measurements on the behavior of a real battery in that situation. Furthermore the results are compared to results of the well-established KiBaM model. Predictions on the SoC over time done using the proposed model closely follow the SoC over time calculated from measured data. The resulting improved model is both simple and effective, making it specially useful as part of smart control, and energy usage simulations.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Decision Tree for State of Charge (SOC) Prediction of LiFePO4 Battery
    Nizam, Muh.
    Mujianto, Agus
    Waloyo, Hery Tri
    Purwanto, Agus
    Inayati
    PROCEEDING JOINT INTERNATIONAL CONFERENCE ON ELECTRIC VEHICULAR TECHNOLOGY AND INDUSTRIAL, MECHANICAL, ELECTRICAL, AND CHEMICAL ENGINEERING (ICEVT & IMECE), 2015, : 359 - 361
  • [32] Electric Vehicle Battery State of Charge Prediction Based on Graph Convolutional Network
    Kim, Geunsu
    Kang, Soohyeok
    Park, Gyudo
    Min, Byung-Cheol
    INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, 2023, 24 (06) : 1519 - 1530
  • [33] Li-ion Battery State of Charge Estimation Based on Comprehensive Kalman Filter
    Gu M.
    Xia C.
    Tian C.
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2019, 34 (02): : 419 - 426
  • [34] Battery state of charge estimation based on multi-model fusion
    Wang, Qiang
    2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 2036 - 2041
  • [35] A Novel Fusion Model for Battery Online State of Charge (SOC) Estimation
    Li, Yufang
    Xu, Guofang
    Xu, Bingqin
    Zhang, Yumei
    INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE, 2021, 16 (01): : 1 - 15
  • [36] Comparison and Evaluation of State of Charge Estimation Methods for a Verified Battery Model
    Nemounehkhah, Behrooz
    Faranda, Roberto
    Akkala, Kishore
    Hafezi, Hossein
    Parthasarathy, Chethan
    Laaksonen, Hannu
    2020 INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES (SEST), 2020,
  • [37] Comprehensive Review of Lithium-Ion Battery State of Charge Estimation by Sliding Mode Observers
    Behnamgol, Vahid
    Asadi, Mohammad
    Mohamed, Mohamed A. A.
    Aphale, Sumeet S.
    Niri, Mona Faraji
    ENERGIES, 2024, 17 (22)
  • [38] Lithium-ion battery state of charge prediction based on machine learning approach
    Zazoum, Bouchaib
    ENERGY REPORTS, 2023, 9 : 1152 - 1158
  • [39] A comprehensive review on the state of charge estimation for lithium-ion battery based on neural network
    Cui, Zhenhua
    Wang, Licheng
    Li, Qiang
    Wang, Kai
    INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2022, 46 (05) : 5423 - 5440
  • [40] BATTERY STATE OF CHARGE AND CHARGE CONTROL-SYSTEM
    VISWANATHAN, S
    CHARKEY, A
    KLEIN, M
    JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 1985, 132 (08) : C337 - C337