05115ntm a2200337 i 4500 0 0 lcc 0 1 20000 20000 2024-08-20 0 FTKMA .H37 2024 r Thesis T000003259 2024-08-20 00:00:00 1 2024-08-20 THESIS 0 0 lcc 0 0 20000 20000 2024-08-20 0 CD13632 T000003260 2024-08-20 00:00:00 1 2024-08-20 THESIS 101145 101151 MY-KuUP 20251125110917.0 t||||fr|||| 000 0 ta 240820t20242024my a|||fr|||| 000 0 eng d THE0009911 (Local) Hardback UMPSA eng UMP rda FTKMA .H37 2024 r Thesis Hasan Najafi Khaboshan, author. Thermal management analysis of lithium-ion battery using passive methods : numerical and artificial intelligence approaches / Hasan Najafi Khaboshan Kuantan, Pahang : UMPSA, 2024 © 2024 xv, 107 pages : illustrations ; 30 cm. + 1 CD-ROM rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Faculty of Mechanical and Automotive Engineering Technology Thesis (Master of Science) -- Universiti Malaysia Pahang – 2024 Includes bibliographical references The development of an efficient battery thermal management system (BTMS) to maintain temperatures of lithium-ion batteries in a specific range has gained significant interest, particularly for electric vehicle applications. A failed BTMS will cause thermal runaway and potential explosions in batteries due to an increment in the temperature of batteries, which endangers the lives of occupants. Using phase change materials (PCMs) in a BTMS is a technique that can control the temperature of batteries during fast battery discharging. However, the thermal conductivity of PCMs is low to provide an excellent heat transfer within the system under harsh conditions. Hence, this research is conducted to analyze the cooling performance enhancement of a PCM-based battery thermal management system with the combinations of fins and metal foam using computational fluid dynamics. Four distinct BTMS configurations are investigated, considering PCM, fins, and metal foam. Furthermore, the effect of various materials of BTMS combination, different fin shapes, and various fin lengths on the performance of the selected BTMS have been investigated. Finally, to find easier and faster methods instead of numerical simulation, the ability of artificial intelligence to predict the average battery temperature and PCM liquid fraction has been analyzed. The analysis is considered under harsh and normal environmental conditions during the discharging process with a 3C current rate. To model the behavior of the PCM, the enthalpy-porosity method is utilized. In the numerical simulations, a two-equation non-equilibrium thermal model is utilized, that offers improved accuracy in capturing heat transfer between the metal foam and PCM compared to traditional thermal equilibrium models. Besides, the validation of the numerical simulation revealed that there is a good agreement between the current numerical findings and previous numerical and experimental data. Results demonstrated that the optimal BTMS configuration, which combines PCM, fins, and metal foam (fourth case), achieves a reduction of 3 K, which is about 1% reduction in the battery temperature. Moreover, 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. Additionally, the optimum case exhibits a maximum delay of approximately 470 seconds in PCM melting. The fins employed in BTMS function as a heat sources network, effectively distributing heat throughout the system; while the utilization of metal foam ensures uniform heat distribution between the battery and the surrounding environment. Furthermore, the findings indicated that utilizing copper fins and copper metal foam leads to the lowest battery surface temperature compared to other material combinations. Examining the impact of various fin shapes on the optimal BTMS performance revealed minimal variations in the battery temperature across different fin shapes. It appears challenging to identify a single fin shape suitable for all environmental conditions. Additionally, when examining the effect of fins length on the performance of the fourth BTMS configuration, it was observed that increasing the fins length results in a decrease in the battery temperature. Lastly, the developed artificial neural network model demonstrated excellent prediction capability, achieving high R-squared values which were 0.98 for the liquid fraction of PCM and 0.99 for the battery surface temperature. To investigate a BTMS utilizing PCM, metal foam, and fins, further work should be studied on this BTMS as a battery pack. In addition, the heat generation of the battery can be considered with the electrochemical models in future works Faculty of Mechanical and Automotive Engineering Technology Dissertations Universities and colleges Dissertations Theses Dissertations lcc THESIS