An improved thermal single particle model and parameter estimation for high-capacity battery cell

被引:12
作者
Hong, Changbeom [1 ]
Cho, Hyeonwoo [1 ]
Hong, Daeki [2 ]
Oh, Se-Kyu [2 ]
Kim, Yeonsoo [1 ]
机构
[1] Kwangwoon Univ, Dept Chem Engn, 20 Kwangwoon Ro, Seoul 01897, South Korea
[2] Hyundai Motor Co, Vehicle Control Technol Dev Team, Hwaseong 18280, South Korea
基金
新加坡国家研究基金会;
关键词
Single particle model; Battery temperature; High-capacity battery; Parameter estimation; Genetic algorithm; DEGRADATION PHYSICS; CHARGE ESTIMATION; ION; STATE; ELECTROLYTE;
D O I
10.1016/j.electacta.2022.141638
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
Single particle model (SPM) is an electrochemical model which can be used with energy balance to calculate the state of charge, terminal voltage, and temperature of the battery. The conventional SPM neglects the lithium -ion dynamics in the electrolyte, and the energy balance usually does not consider the heat transfer delay from the battery center to the surface. These lead to model errors when SPM is used for high-capacity battery cells. In this study, we propose several strategies to improve the model accuracy for high-capacity battery cells. First, the actual discharge capacity is considered by modifying the desired Li-ion flux at the electrode surface. Second, a part of the equivalent circuit model is integrated with SPM to consider the resistance in the electrolyte computationally efficiently. Third, the delay in the heat transfer from the center to the surface is represented using a second-order system dynamic. In electric vehicles, battery cells are stacked as a pack; we account for the additional heat generated by stacking pressure-induced swelling repression. Finally, parameter estimation is conducted to determine the best parameter values of the model using the experimental data. The reference values are set using a dimensionless scale-up approach. When the parameters are estimated and the model is tested with the validation data, the error percentages in the calculated terminal voltage and temperature are smaller than 2.0410% and 3.5032%, respectively, even with the cell variances.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] Enhanced parameter estimation with improved particle swarm optimization algorithm for cell culture process modeling
    Fu, Zhongwang
    Wang, Zheyu
    Chen, Gong
    AICHE JOURNAL, 2024, 70 (04)
  • [22] A Improved Particle Swarm optimization and Its Application in the Parameter Estimation
    Wu Tiebin
    Cheng Yun
    Hu Zhikun
    Zhou Taoyun
    Liu Yunlian
    MECHATRONICS, ROBOTICS AND AUTOMATION, PTS 1-3, 2013, 373-375 : 1150 - +
  • [23] Layering Charged Polymers Enable Highly Integrated High-Capacity Battery Anodes
    Han, Dong-Yeob
    Han, Im Kyung
    Son, Hye Bin
    Kim, Youn Soo
    Ryu, Jaegeon
    Park, Soojin
    ADVANCED FUNCTIONAL MATERIALS, 2023, 33 (17)
  • [24] Optimal Parameter Estimation of Battery Model for Pivotal Automotive Battery Management System
    Sangwan, Venu
    Sharma, Avinash
    Kumar, Rajesh
    Rathore, Akshay Kumar
    2017 1ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2017 17TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE), 2017,
  • [25] Evaluating thermal response tests using parameter estimation for thermal conductivity and thermal capacity
    Wagner, R
    Clauser, C
    JOURNAL OF GEOPHYSICS AND ENGINEERING, 2005, 2 (04) : 349 - 356
  • [26] Improved range model based on parameter estimation for high resolution and high squint LEO SAR
    Gu, Tong
    Liao, Gui Sheng
    Yang, Zhi Wei
    Guo, Yi Fan
    JOURNAL OF ENGINEERING-JOE, 2019, 2019 (19): : 5693 - 5696
  • [27] Battery internal temperature estimation via a semilinear thermal PDE model
    Zhang, Dong
    Dey, Satadru
    Tang, Shu-Xia
    Drummond, Ross
    Moura, Scott J.
    AUTOMATICA, 2021, 133
  • [28] New method for parameter estimation of an electrochemical-thermal coupling model for LiCoO2 battery
    Li, Junfu
    Wang, Lixin
    Lyu, Chao
    Wang, Han
    Liu, Xuan
    JOURNAL OF POWER SOURCES, 2016, 307 : 220 - 230
  • [29] Hybrid battery model parameter estimation and optimization using a two-step procedure and parameter sensitivity analysis
    Liu, Enhui
    Sun, Leo
    Anderson-McLeod, Alex
    2023 IEEE 2ND INDUSTRIAL ELECTRONICS SOCIETY ANNUAL ON-LINE CONFERENCE, ONCON, 2023,
  • [30] Vanadium redox battery parameter estimation using electrochemical model by reduced number of sensors
    Khaki, Bahman
    Sawant, Rutvika
    Das, Pritam
    ENERGY STORAGE, 2023, 5 (04)