Battery thermal management: An optimization study of parallelized conjugate numerical analysis using Cuckoo search and Artificial bee colony algorithm. (February 2021) - Afzal, Asif; Samee, A.D. Mohammed; Jilte, R.D.; Islam, Md. Tariqul; Manokar, A. Muthu; Abdul Razak, Kaladgi
Highlights: Conjugate thermal analysis and optimization is carried using several coolants. The numerical method is parallelized using OpenMP for faster results. Single and multi-objective optimization of thermal management characteristics is done. Cuckoo search optimization and artificial bee colony algorithm is used. Nanofluids and thermal oils have emerged as the best coolants for optimized thermal characteristics. Abstract: Thermal management of heat-generating battery packs involve many operating parameters affecting its performance, efficiency, and maintenance. Heat generation ( Qgen ), conductivity ratio ( Cr ), Reynolds number ( Re ), spacing between the packs ( Ws ), and coolant Prandtl number ( Pr ) are the parameters selected as working parameters for conjugate thermal analysis and optimization. The thermal analysis of battery packs is carried out numerically using the finite volume method. Single and multi-objective optimization of thermal management characteristics, namely maximum temperature ( Tb, max ), average Nusselt number ( Nuavg ), and coefficient of friction ( Fcavg ) using Cuckoo search (CS) and artificial bee colony (ABC) algorithm is attempted. For faster numerical analysis, the developed code is parallelized using OpenMP paradigm. 25 coolants having Pr in the range 0.02 to 511.5 belonging to five categories i.e. gases, oils, thermal oils, nanofluids, and liquid metals, are adopted for optimization. Nuavg and Fcavg are not affected by Cr and Qgen, while Tb, max changes significantly. Ws, Pr, and Re impact these characters differently, demanding the need for optimization. Nanofluids and thermal oils have emerged as the best coolants for optimized thermal characteristics at higher heat generations. CS algorithm provided high fitness of objective functions in single-objective optimization, whereas the ABC algorithm converged with high fitness during multi-objective optimization.