Modeling and optimization of micro heat pipe cooling battery thermal management system via deep learning and multi-objective genetic algorithms
•A comprehensive model based on battery aging effect is developed for MHP-BTMS.•A framework combing deep learning and multi-objective genetic algorithm.•BTMS shows poor cooling capacity due to SEI formation inside the aged battery.•Two optimization strategies are proposed for multi-objective optimiz...
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| Vydáno v: | International journal of heat and mass transfer Ročník 207; s. 124024 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
15.06.2023
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| Témata: | |
| ISSN: | 0017-9310, 1879-2189 |
| On-line přístup: | Získat plný text |
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| Abstract | •A comprehensive model based on battery aging effect is developed for MHP-BTMS.•A framework combing deep learning and multi-objective genetic algorithm.•BTMS shows poor cooling capacity due to SEI formation inside the aged battery.•Two optimization strategies are proposed for multi-objective optimization.•The trade-off between battery temperature and performance is achieved.
Battery thermal management and electrochemical performance are critical for efficient and safe operation of battery pack. In this research, a multi-physics model considering the battery aging effect is developed for micro heat pipe battery thermal management system (MHP-BTMS). A novel multi-variables global optimization framework combining multi-physics modeling, deep learning and multi-objective optimization algorithms is established for optimizing the structural parameters of MHP-BTMS to improve battery thermal management and electrochemical performance simultaneously. It is found that MHP-BTMS fails to control the temperature of aged battery pack due to the higher heat generation caused by solid electrolyte interphase formation. After 1000 cycles, the maximum temperature and maximum temperature difference were increased by 3.32 K, 2.49 K, 2.04 K and 1.78 K, 1.46 K, 1.26 K, respectively. It is also found that the battery electrochemical performance during the cycling is highly related to battery thermal behaviors. MHP-BTMS with 0.004/s inlet velocity achieved the best performance in preventing SEI formation and battery aging effect, which was lower by 7.01 nm (SEI) and 1.65% (aging), 2.31 nm and 0.58% as compared to 0.002 and 0.003 m/s cases. Besides, MHP-BTMS with optimized inlet velocity, MHP arrangement and cold plate can improve cooling performance and electrochemical performance. Multi-variables global optimization can provide the optimal structure parameters of MHP-BTMS under the different combinations of weighted coefficients and optimization strategies to achieve the trade-off between battery thermal issues and electrochemical performance. In addition, it is demonstrated that the weighted coefficients and optimization strategies in this novel framework can be changed according to the actual needs in engineering applications. |
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| AbstractList | •A comprehensive model based on battery aging effect is developed for MHP-BTMS.•A framework combing deep learning and multi-objective genetic algorithm.•BTMS shows poor cooling capacity due to SEI formation inside the aged battery.•Two optimization strategies are proposed for multi-objective optimization.•The trade-off between battery temperature and performance is achieved.
Battery thermal management and electrochemical performance are critical for efficient and safe operation of battery pack. In this research, a multi-physics model considering the battery aging effect is developed for micro heat pipe battery thermal management system (MHP-BTMS). A novel multi-variables global optimization framework combining multi-physics modeling, deep learning and multi-objective optimization algorithms is established for optimizing the structural parameters of MHP-BTMS to improve battery thermal management and electrochemical performance simultaneously. It is found that MHP-BTMS fails to control the temperature of aged battery pack due to the higher heat generation caused by solid electrolyte interphase formation. After 1000 cycles, the maximum temperature and maximum temperature difference were increased by 3.32 K, 2.49 K, 2.04 K and 1.78 K, 1.46 K, 1.26 K, respectively. It is also found that the battery electrochemical performance during the cycling is highly related to battery thermal behaviors. MHP-BTMS with 0.004/s inlet velocity achieved the best performance in preventing SEI formation and battery aging effect, which was lower by 7.01 nm (SEI) and 1.65% (aging), 2.31 nm and 0.58% as compared to 0.002 and 0.003 m/s cases. Besides, MHP-BTMS with optimized inlet velocity, MHP arrangement and cold plate can improve cooling performance and electrochemical performance. Multi-variables global optimization can provide the optimal structure parameters of MHP-BTMS under the different combinations of weighted coefficients and optimization strategies to achieve the trade-off between battery thermal issues and electrochemical performance. In addition, it is demonstrated that the weighted coefficients and optimization strategies in this novel framework can be changed according to the actual needs in engineering applications. |
| ArticleNumber | 124024 |
| Author | Wang, Yang Zhao, Tianshou Zhao, Siyuan Guo, Zengjia Ni, Meng |
| Author_xml | – sequence: 1 givenname: Zengjia surname: Guo fullname: Guo, Zengjia organization: Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD), Research Institute for Smart Energy (RISE), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China – sequence: 2 givenname: Yang orcidid: 0000-0002-1180-7403 surname: Wang fullname: Wang, Yang organization: Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD), Research Institute for Smart Energy (RISE), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China – sequence: 3 givenname: Siyuan surname: Zhao fullname: Zhao, Siyuan organization: Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD), Research Institute for Smart Energy (RISE), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China – sequence: 4 givenname: Tianshou surname: Zhao fullname: Zhao, Tianshou email: zhaots@sustech.edu.cn organization: Department of Mechanical and Energy Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, China – sequence: 5 givenname: Meng orcidid: 0000-0001-5310-4039 surname: Ni fullname: Ni, Meng email: meng.ni@polyu.edu.hk organization: Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD), Research Institute for Smart Energy (RISE), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China |
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| Keywords | Battery aging Deep learning Lithium-ion battery Battery thermal management Multiphysics modeling Multi-objective optimization |
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