TY - JOUR
T1 - Modeling and optimization of liquid-based battery thermal management system considering battery electrochemical characteristics
AU - Guo, Zengjia
AU - Wang, Yang
AU - Zhao, Siyuan
AU - Zhao, Tianshou
AU - Ni, Meng
N1 - Funding Information:
This research is supported by a grant under the Theme-based Scheme (project number: T23-601/17-R ) from Research Grants Council, University Grants Committee , Hong Kong SAR.
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/10/15
Y1 - 2023/10/15
N2 - In this research, a novel multi-physics model is proposed to facilitate battery thermal management system (BTMS) design and optimization. For parametric simulations and optimization, a new optimization framework is developed for BTMS by utilizing numerical simulations, artificial intelligence and multi-objective genetic algorithm. It is found that BTMS is inadequate in addressing thermal issues that arise in aged battery pack, primarily because of the increased total heat generation rate resulting from battery aging effect. Besides, it has also been observed that BTMS plays a significant role in managing battery electrochemical performance. Meanwhile, optimizing mini-channel geometrical parameters, mini-channel arrangement and nanoparticle volume fraction are found to be an effective method to further control battery thermal issues. However, it is observed that reducing battery temperature invariably incurs a reduction in battery average potential. Therefore, multi-variables global optimizations are conducted based on various combinations of weighted coefficients and optimization strategies. It is found that all obtained optimization schemes can achieve the trade-offs among battery thermal behaviors, pressure loss and electrochemical performance, with meeting the desired temperature requirements even during long-term operation. Furthermore, the selection about weighted coefficient and optimization strategy can be tailored to meet the specific demands and prerequisites of various engineering applications.
AB - In this research, a novel multi-physics model is proposed to facilitate battery thermal management system (BTMS) design and optimization. For parametric simulations and optimization, a new optimization framework is developed for BTMS by utilizing numerical simulations, artificial intelligence and multi-objective genetic algorithm. It is found that BTMS is inadequate in addressing thermal issues that arise in aged battery pack, primarily because of the increased total heat generation rate resulting from battery aging effect. Besides, it has also been observed that BTMS plays a significant role in managing battery electrochemical performance. Meanwhile, optimizing mini-channel geometrical parameters, mini-channel arrangement and nanoparticle volume fraction are found to be an effective method to further control battery thermal issues. However, it is observed that reducing battery temperature invariably incurs a reduction in battery average potential. Therefore, multi-variables global optimizations are conducted based on various combinations of weighted coefficients and optimization strategies. It is found that all obtained optimization schemes can achieve the trade-offs among battery thermal behaviors, pressure loss and electrochemical performance, with meeting the desired temperature requirements even during long-term operation. Furthermore, the selection about weighted coefficient and optimization strategy can be tailored to meet the specific demands and prerequisites of various engineering applications.
KW - Artificial intelligence
KW - Battery temperature
KW - Capacity fade
KW - Global optimization
UR - http://www.scopus.com/inward/record.url?scp=85162241386&partnerID=8YFLogxK
U2 - 10.1016/j.est.2023.108028
DO - 10.1016/j.est.2023.108028
M3 - Journal article
AN - SCOPUS:85162241386
SN - 2352-152X
VL - 70
JO - Journal of Energy Storage
JF - Journal of Energy Storage
M1 - 108028
ER -