Najafi Khaboshan, Hasan and Jaliliantabar, Farzad and Abdul Adam, Abdullah and Panchal, Satyam and Azarinia, Amiratabak (2024) Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models. Applied Thermal Engineering, 247 (123080). pp. 1-13. ISSN 1359-4311. (Published)
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Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus & WOS |
| Uncontrolled Keywords: | Artificial neural network model; Battery thermal management system; Computational fluid dynamics; Cooling performance enhancement; Lithium-ion battery |
| Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TL Motor vehicles. Aeronautics. Astronautics |
| Faculty/Division: | Centre of Excellence: Centre of Excellence for Advanced Research in Fluid Flow Institute of Postgraduate Studies Centre of Excellence: Automotive Engineering Centre Centre of Excellence: Automotive Engineering Centre Faculty of Mechanical and Automotive Engineering Technology |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 21 May 2024 06:04 |
| Last Modified: | 23 May 2024 00:24 |
| URI: | http://umpir.ump.edu.my/id/eprint/41336 |
| Download Statistic: | View Download Statistics |
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