Structural optimisation design of liquid cooling system for lithium-ion battery based on improved Kriging method

Abstract

The battery thermal management system effectively limits the temperature of each lithium-ion battery (LIB) to below 45°C and minimises the temperature difference between different LIBs to extend their service life. Given the volume constraints, the finite element method (FEM) was used to perform the structural optimisation calculation of battery thermal management systems (BTMS). However, owing to their high calculation costs, optimisation methods based on surrogate models are preferred. The k-means clustering strategy of the stochastic reduced-order model (SROM) method, as implemented within the domain of uncertainty analysis, was shown in this study to enhance the initial observation point sampling strategy of the Kriging optimisation method. The use of an active sampling strategy has been demonstrated to enhance the representativeness of observation points with respect to the overall grid points, which in turn accelerates the convergence rate of the Kriging optimisation method. In the multiphysics simulation example of an LIB liquid cooling system modelled in COMSOL software, the relative error of the improved Kriging method is reduced from 0.24% to 0.11% compared with the traditional Kriging method, and the calculation efficiency is increased by 86.7%. This provided a quantitative verification of the effectiveness of the proposed method.