Integrated Equivalent Circuit and Thermal Model for Simulation of Temperature-Dependent LiFePO4 Battery in Actual Embedded Application

被引:51
作者
Gao, Zuchang [1 ]
Chin, Cheng Siong [2 ]
Woo, Wai Lok [3 ]
Jia, Junbo [1 ]
机构
[1] Temasek Polytech, Sch Engn, 21 Tampines Ave 1, Singapore 529757, Singapore
[2] Newcastle Univ, Sch Marine Sci & Technol, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
[3] Newcastle Univ, Sch Elect & Elect Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
关键词
lithium-ion battery; battery management system; convective thermal model; cell model; state-of-charge; LITHIUM-ION BATTERIES; OF-CHARGE ESTIMATION; NEURAL-NETWORK; STATE; EKF;
D O I
10.3390/en10010085
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
A computational efficient battery pack model with thermal consideration is essential for simulation prototyping before real-time embedded implementation. The proposed model provides a coupled equivalent circuit and convective thermal model to determine the state-of-charge (SOC) and temperature of the LiFePO4 battery working in a real environment. A cell balancing strategy applied to the proposed temperature-dependent battery model balanced the SOC of each cell to increase the lifespan of the battery. The simulation outputs are validated by a set of independent experimental data at a different temperature to ensure the model validity and reliability. The results show a root mean square (RMS) error of 1.5609 x 10(-5) for the terminal voltage and the comparison between the simulation and experiment at various temperatures (from 5 degrees C to 45 degrees C) shows a maximum RMS error of 7.2078 x 10(-5).
引用
收藏
页数:22
相关论文
共 20 条
  • [1] New Electro-Thermal Battery Pack Model of an Electric Vehicle
    Alhanouti, Muhammed
    Giessler, Martin
    Blank, Thomas
    Gauterin, Frank
    [J]. ENERGIES, 2016, 9 (07)
  • [2] [Anonymous], P IEEE INT C COMP CO
  • [3] Accuracy improvement of SOC estimation in lithium-ion batteries
    Awadallah, Mohamed A.
    Venkatesh, Bala
    [J]. JOURNAL OF ENERGY STORAGE, 2016, 6 : 95 - 104
  • [4] A New State of Charge Estimation Method for LiFePO4 Battery Packs Used in Robots
    Chang, Ming-Hui
    Huang, Han-Pang
    Chang, Shu-Wei
    [J]. ENERGIES, 2013, 6 (04) : 2007 - 2030
  • [5] Lyapunov-Based Adaptive State of Charge and State of Health Estimation for Lithium-Ion Batteries
    Chaoui, Hicham
    Golbon, Navid
    Hmouz, Imad
    Souissi, Ridha
    Tahar, Sofiene
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 62 (03) : 1610 - 1618
  • [6] State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF
    Charkhgard, Mohammad
    Farrokhi, Mohammad
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2010, 57 (12) : 4178 - 4187
  • [7] State-of-charge determination from EMF voltage estimation: Using impedance, terminal voltage, and current for lead-acid and lithium-ion batteries
    Coleman, Martin
    Lee, Chi Kwan
    Zhu, Chunbo
    Hurley, William Gerard
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2007, 54 (05) : 2550 - 2557
  • [8] Dynamic model of lithium polymer battery - Load resistor method for electric parameters identification
    Gandolfo, Daniel
    Brandao, Alexandre
    Patino, Daniel
    Molina, Marcelo
    [J]. JOURNAL OF THE ENERGY INSTITUTE, 2015, 88 (04) : 470 - 479
  • [9] Support vector based battery state of charge estimator
    Hansen, T
    Wang, CJ
    [J]. JOURNAL OF POWER SOURCES, 2005, 141 (02) : 351 - 358
  • [10] State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation
    He, Wei
    Williard, Nicholas
    Chen, Chaochao
    Pecht, Michael
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 62 : 783 - 791