Battery parameter identification method of a battery module based on a multi-physical measurement system

被引:0
|
作者
Li, Xiaoyu [1 ,3 ]
Chen, Fengyi [3 ]
Lin, Shaohong [3 ]
Huang, Zhijia [2 ]
Tian, Yong [3 ]
Tian, Jindong [3 ]
机构
[1] Shenzhen Univ, State Key Lab Radio Frequency Heterogeneous Integr, Shenzhen 518060, Peoples R China
[2] Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
[3] Shenzhen Univ, Coll Phys & Optoelect Engn, Key Lab Optoelect Devices & Syst, Minist Educ & Guangdong Prov, Shenzhen 518060, Peoples R China
基金
中国国家自然科学基金;
关键词
Multiphysics; Data fusion; Battery module; Modelling; Parameter identification; EQUIVALENT-CIRCUIT MODELS; ION BATTERY; CHARGE ESTIMATION; STATE; OPTIMIZATION; ALGORITHM;
D O I
10.1016/j.est.2023.110216
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
The secondary utilization of retired electric vehicle batteries is beneficial for improving resource utilization efficiency. Capacity and internal resistance are battery parameters that can reflect the battery state. To identify the parameters of a single battery in a battery module, it is usually necessary to disassemble the battery module. The process is complex, time-consuming, and unsafe. In this paper, a battery parameter identification method without disassembling the battery module is developed based on a multi-physical measurement system. First, a multi-physical electrical-thermal-spatial measurement system is constructed to measure the physical parameters of the battery module during charging and discharging. Then, the electrical model and the thermal model are developed. To obtain the capacity and internal resistance of each cell within the battery module, a battery parameter identification model is established with temperature and total battery current as input parameters and battery capacity and internal resistance as output parameters. Batteries with different capacities and internal resistances are connected in series and parallel to simulate retired battery modules for electric vehicles. The battery parameters are identified using the method presented in this paper. According to the experiments, the relative errors of parameter identification for battery capacity and internal resistance are both within 9 %. This method can improve the efficiency of the battery screening process as well as the reliability of the battery system.
引用
收藏
页数:12
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