Multi-objective optimization design of thermal management system for lithium-ion battery pack based on Non-dominated Sorting Genetic Algorithm II

The thermal management of batteries was a significant issue considering the safety and efficiency. Optimal design of a novel liquid cooling system with symmetrical double-layer reverting bifurcation channel was performed by combining experimental, numerical simulation and multi-objective optimization techniques. The thermophysical parameters and heat production rate of the battery for numerical simulation were obtained by experiments. The convective heat transfer coefficient and the surface friction coefficient were chosen as objective functions to visually reflect the heat transfer process. Furthermore, batteries were confined to work at the optimal temperature (25-40 degrees C) and the optimal temperature difference between cells (less than 5 degrees C). The performance values of design points obtained by Latin hypercube sampling were calculated numerically. Response surface approximation was adopted to approximate the objective function and the constraint function to reduce computing time. The Pareto-optimal front between -h and f was obtained using Non-dominated Sorting Genetic Algorithm II. 17.19% change in heat transfer coefficient was accomplished by 85.53% change in skin friction coefficient. The results reported that the cooling system with optimized thermal performance can be obtained at low flow loss.