Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models

被引:91
作者
Khaboshan, Hasan Najafi [1 ]
Jaliliantabar, Farzad [1 ,2 ,3 ]
Abdullah, Abdul Adam [1 ,2 ]
Panchal, Satyam [4 ]
Azarinia, Amiratabak [5 ]
机构
[1] Univ Malaysia Pahang, Fac Mech & Automot Engn Technol, Pekan 26600, Pahang, Malaysia
[2] Univ Malaysia Pahang, Automot Engn Ctr, Pekan 26600, Pahang, Malaysia
[3] Univ Malaysia Pahang, Ctr Excellence Adv Res Fluid Flow CARIFF, Kuantan 26300, Pahang, Malaysia
[4] Univ Waterloo, Mech & Mechatron Engn Dept, Waterloo, ON, Canada
[5] KN Toosi Univ Technol, Dept Aerosp Engn, Tehran, Iran
关键词
Battery thermal management system; Lithium-ion battery; Computational fluid dynamics; Artificial neural network model; Cooling performance enhancement; MODULE;
D O I
10.1016/j.applthermaleng.2024.123080
中图分类号
O414.1 [热力学];
学科分类号
摘要
The focus on developing an effective battery thermal management system (BTMS) to maintain optimal temperatures for lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications, has grown significantly. The effective BTMS not only enhances the cooling performance of LIBs but also contributes to increased passenger safety and mileage of EVs. This study investigates BTMS configurations with fins, metal foam, and phase change material (PCM) to minimize temperature of battery during 3C discharging in varying conditions. Additionally, the study explores the impact of different BTMS material combinations and various fins lengths on system performance as a parametric investigation. Moreover, to streamline the analysis process and introduce novelty, artificial intelligence is explored as an alternative to computational fluid dynamics for predicting liquid fraction of PCM and temperature of battery, enhancing the innovative aspect of this study. Numerical simulations, using a non-equilibrium thermal model for metal foam modeling, reveal that the fourth case, integrating all three passive approaches, maintains the lowest temperature and enhances LIB cooling. The optimum BTMS shows a reduction of 3 K compared to BTMS utilizing pure PCM. During discharge process, the temperature difference in the battery decreases by approximately 75 % and 66 % in the fourth case compared to the first case (with pure PCM) under normal and harsh environmental conditions, respectively. Applying copper metal foam and copper fins yields the best results in reducing battery temperature. Increasing the length of fins and adding more fins effectively lower the battery temperature. Finally, an artificial neural network model is developed using the backpropagation learning technique coupled with the gradient descent optimization algorithm. The model exhibits excellent predictive capabilities, achieving high R-squared values of 0.98 for PCM liquid fraction and 0.99 for battery temperature.
引用
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页数:13
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