An extended single-particle model of lithium-ion batteries based on simplified solid-liquid diffusion process

被引:1
作者
He, Wei [1 ]
Han, Tao [1 ]
Song, Haiqin [2 ,3 ]
Wang, Qian [4 ]
Kang, Jianqiang [2 ,3 ]
Wang, Jing, V [4 ]
Chen, Weihua [5 ,6 ]
机构
[1] Three Gorges Elect Energy Co Ltd, Wuhan 430000, Hubei, Peoples R China
[2] Hubei Key Lab Adv Technol Automot Components, Wuhan 430070, Hubei, Peoples R China
[3] Hubei Collaborat Innovat Ctr Automot Components Te, Wuhan 430070, Hubei, Peoples R China
[4] Wuhan Univ Technol, Sch Automat, Wuhan 430070, Hubei, Peoples R China
[5] Zhengzhou Univ, Coll Chem, Zhengzhou 450001, Peoples R China
[6] Zhengzhou Univ, Green Catalysis Ctr, Zhengzhou 450001, Peoples R China
基金
中国国家自然科学基金;
关键词
SIMULATION; CHARGE; CELL;
D O I
10.1016/j.isci.2024.110764
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
An extended lithium-ion battery model is proposed, which simplifies the solid-liquid diffusion process compared with the full-order pseudo two-dimensional (P2D) model, in order to reduce computational complexity and enhance modeling speed. To simplify the model, the three-parameter method is utilized to simplify the solid-phase diffusion process. Meanwhile, exponential fitting is applied to simplify the liquid-phase diffusion process. In addition, the average current flux is used to simplify the current exchange density at the solid-liquid interface. Finally, experiments are performed to verify the model, and experimental results demonstrate that, even though fewer parameters need to be identified, the simplified model still has satisfied accuracy under constant current and dynamic situations.
引用
收藏
页数:21
相关论文
共 21 条
[1]   A computationally efficient model for performance prediction of lithium-ion batteries [J].
Amiri, Mahshid Nejati ;
Torabi, Farschad .
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2021, 43
[2]   Online state of health and aging parameter estimation using a physics-based life model with a particle filter [J].
Bi, Yalan ;
Yin, Yilin ;
Choe, Song-Yul .
JOURNAL OF POWER SOURCES, 2020, 476
[3]   Lithium-Ion Battery State of Charge and Critical Surface Charge Estimation Using an Electrochemical Model-Based Extended Kalman Filter [J].
Di Domenico, Domenico ;
Stefanopoulou, Anna ;
Fiengo, Giovanni .
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, 2010, 132 (06)
[4]   MODELING OF GALVANOSTATIC CHARGE AND DISCHARGE OF THE LITHIUM POLYMER INSERTION CELL [J].
DOYLE, M ;
FULLER, TF ;
NEWMAN, J .
JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 1993, 140 (06) :1526-1533
[5]   Systematic parameter identification of a control-oriented electrochemical battery model and its application for state of charge estimation at various operating conditions [J].
Fan, Guodong .
JOURNAL OF POWER SOURCES, 2020, 470
[6]  
Ge H., 2022, Lithium-ion Battery AC Preheating and Rapid Charging to Suppress Lithium Precipitation at Low Temperatures238, DOI [10.1016/j.energy.2021.121809, DOI 10.1016/J.ENERGY.2021.121809]
[7]  
Han X.B., 2014, Research on the Mechanism Model and State Estimation of Automotive Lithium Ion Batteries, V10, P578, DOI [10.1016/j.jtte.2023.06.001, DOI 10.1016/J.JTTE.2023.06.001]
[8]   Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence [J].
Li, Weihan ;
Demir, Iskender ;
Cao, Decheng ;
Joest, Dominik ;
Ringbeck, Florian ;
Junker, Mark ;
Sauer, Dirk Uwe .
ENERGY STORAGE MATERIALS, 2022, 44 :557-570
[9]   Kinetic study on LiFePO4/C nanocomposites synthesized by solid state technique [J].
Liu, H. ;
Li, C. ;
Zhang, H. P. ;
Fu, L. J. ;
Wu, Y. P. ;
Wu, H. Q. .
JOURNAL OF POWER SOURCES, 2006, 159 (01) :717-720
[10]   An approximate solution for electrolyte concentration distribution in physics-based lithium-ion cell models [J].
Luo, Weilin ;
Lyu, Chao ;
Wang, Lixin ;
Zhang, Liqiang .
MICROELECTRONICS RELIABILITY, 2013, 53 (06) :797-804