共 31 条
An improved single particle model for lithium-ion batteries based on main stress factor compensation
被引:51
作者:
Tian, Jiaqiang
[1
]
Wang, Yujie
[1
]
Chen, Zonghai
[1
]
机构:
[1] Univ Sci & Technol China, Dept Automat, Hefei 230027, Anhui, Peoples R China
基金:
中国国家自然科学基金;
关键词:
Lithium-ion battery;
Single particle model;
Main stress factors;
Orthogonal stress experiment;
Compensated model;
STATE-OF-CHARGE;
ELECTROCHEMICAL PERFORMANCE;
PHYSICS;
DISCHARGE;
CELL;
SIMPLIFICATION;
PREDICTION;
DIFFUSION;
DESIGN;
D O I:
10.1016/j.jclepro.2020.123456
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Lithium-ion battery is a complex thermoelectric coupling system. In order to understand its internal mechanism, it is necessary to build an accurate electrochemical model, which can be used for state estimation, life prediction, fault diagnosis. However, many stress factors affect the electrochemical re-action of the battery. The key to establishing an accurate electrochemical model is to determine the main stress factors. Based on the traditional single particle model, this paper studies the main stress factors that affect the polarization process of the battery. The main stress factor is used to compensate and modify the critical parameters of polarization, and a more accurate single particle model is established. Firstly, the concentration polarization process is added to the traditional single particle model. Secondly, through the orthogonal stress experiment, the principal stresses in temperature, current, and SOC are discussed for each polarization process. Thirdly, the parameters of the model are identified by the impulse current test and least-squares fitting, and compensated by the main stress factors. Finally, the compensated model is verified under different experimental conditions. The experimental results show that the current and temperature are the main stress factors affecting the concentration polarization and activation polarization of the cell. Considering the non-main factor SOC not only does not significantly improve the model accuracy, but also increases the workload of model parameter identification. The compensated model has better accuracy and adaptability than the original model. (C) 2020 Elsevier Ltd. All rights reserved.
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页数:12
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