Multi-objective optimization design of double-layered reverting cooling plate for lithium-ion batteries

Abstract

To keep the operating temperature of Li-ion battery for electric vehicles in the optimum range, a novel double-layer reverting channel was proposed, which included the collecting layer channel and the dispersed layer channel. Furthermore, the structural theory was invoked to design the reverting channel to reduce the pressure drop. To obtain the optimal cold plate structure and achieve the trade-off between objective functions (maximum temperature, surface standard deviation, and pressure drop), a multi-objective optimization design method based on genetic algorithm was applied. Taking the volume occupied by the cooling channel in the cooling plate as the constraint, the width ratio, the length ratios of the X axes and Y axes, and the channel thickness were chosen as the design variables. The Latin Hypercube Sampling (LHS) was used to select 40 design points in the design space. Further, the response surface approximation (RSA) surrogate model was adopted to establish the relationship between objective functions and the design variables. The optimization results were validated by numerical simulation, and they had reached an agreement that the new double-layered cooling channel can ensure the lithium-ion batteries to work in the optimum temperature range. Moreover, the multi-objective optimization design can reduce the maximum temperature, surface temperature standard deviation and pressure drop of the cold plate at the same time.