An extended single particle model-based parameter identification scheme for lithium-ion cells

被引:4
作者
Hui, Pang [1 ]
机构
[1] Xian Univ Technol, Sch Mech & Precis Instrument Engn, Xian 710048, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
lithium-ion cell; extended single particle model; parameter identification; model validation; ELECTROCHEMICAL MODEL; BATTERY; STATE; TIME; OPTIMIZATION; DESIGN;
D O I
10.7498/aps.67.20172171
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The accurate modeling and parameter identification of lithium-ion battery are of great significance in real-time control and high-performance operation for advanced battery management system (BMS) in electrified vehicles (EVs). However, it is difficult to obtain the information about the interior state inside battery, because it cannot be directly measured by some electric devices. In order to accurately identify the key state parameters of lithium-ion cell applied to electric ground vehicles, an extended single particle model of lithium-ion cell with electrolyte dynamics behaviors is first built up based on the porous electrode theory and concentration theory in this article. Compared with the conventional single particle cell model, the parameter description of the solid electrolyte interface film is incorporated into this model, and the coupled effects of temperature-dependent and electrolyte-dependent electrochemical parameters on the cell discharge are also taken into consideration. Based on this extended single particle cell model, a simplified parameter sensitivity analysis method and a comprehensive parameter identification scheme for lithium-ion cell are proposed herein, in which a sensitivity analysis of the capacity to a subset of electrochemical parameters that are hypothesized to evolve throughout the battery's life, is conducted to determine the highly sensitive parameters to be identified under some particular operation scenarios, and further to solve the parameter optimization problem using the genetic algorithm. Based on this method, the test data under the working condition of 1 C discharge rate at 23 degrees C are employed to evaluate the identified parameters of lithium-ion battery cell with a peak value of voltage error less than 3.8%. Afterwards, the effectiveness and feasibility of the proposed parameter identification scheme are validated by the comparative study of the simulated output voltage and the experimental output voltage under the same input current profile. Specifically, the 0.05 C discharge and HPPC (hybrid pulse power characterization) current profile are used to verify the evaluated parameters under the 1 C discharge condition, and the maximum relative errors of voltage with 0.05 C galvanostatic discharge profile at 23 and 45 degrees C are 3.4% and 2.6% by using our proposed SPMe_SEI model, and 5.7% and 4.0% by using the traditional SPMe model, respectively. Moreover, the maximum relative errors of voltage with HPPC discharge profile at 23 and 45 degrees C are 1.9% and 1.5% by using our proposed SPMe_SEI model, and 2.1% and 1.8% by using the traditional SPMe model, respectively. It is concluded that the proposed parameter identification scheme for a lithium-ion cell model can provide a solid theory foundation for facilitating the estimation of state-of-health in BMS application.
引用
收藏
页数:11
相关论文
共 36 条
  • [1] Electrochemical Model-Based State of Charge and Capacity Estimation for a Composite Electrode Lithium-Ion Battery
    Bartlett, Alexander
    Marcicki, James
    Onori, Simona
    Rizzoni, Giorgio
    Yang, Xiao Guang
    Miller, Ted
    [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2016, 24 (02) : 384 - 399
  • [2] Towards real-time (milliseconds) parameter estimation of lithium-ion batteries using reformulated physics-based models
    Boovaragavan, Vijayasekaran
    Harinipriya, S.
    Subramanian, Venkat R.
    [J]. JOURNAL OF POWER SOURCES, 2008, 183 (01) : 361 - 365
  • [3] Online Parameter Identification of Lithium-Ion Batteries With Surface Temperature Variations
    Chaoui, Hicham
    El Mejdoubi, Asmae
    Gualous, Hamid
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (03) : 2000 - 2009
  • [4] Application status and future of multi-scale numerical models for lithium ion battery
    Cheng Yun
    Li Jie
    Jia Ming
    Tang Yi-Wei
    Du Shuang-Long
    Ai Li-Hua
    Yin Bao-Hua
    Ai Liang
    [J]. ACTA PHYSICA SINICA, 2015, 64 (21)
  • [5] Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales
    Dai, Haifeng
    Xu, Tianjiao
    Zhu, Letao
    Wei, Xuezhe
    Sun, Zechang
    [J]. APPLIED ENERGY, 2016, 184 : 119 - 131
  • [6] Di D, 2009, J DYN SYST-T ASME, V132, P768
  • [7] Diwakar V D, 2009, THESIS
  • [8] Domenico D, 2009, J DYN SYST-T ASME, V132, P768
  • [9] THE USE OF MATHEMATICAL-MODELING IN THE DESIGN OF LITHIUM POLYMER BATTERY SYSTEMS
    DOYLE, M
    NEWMAN, J
    [J]. ELECTROCHIMICA ACTA, 1995, 40 (13-14) : 2191 - 2196
  • [10] Modeling of Li-Ion Cells for Fast Simulation of High C-Rate and Low Temperature Operations
    Fan, Guodong
    Pan, Ke
    Canova, Marcello
    Marcicki, James
    Yang, Xiao Guang
    [J]. JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 2016, 163 (05) : A666 - A676