关键词: 动力电池, 激光焊接, 工艺参数, 核岭回归, 多目标粒子群优化

Abstract: The selection of process parameters is a difficult problem in current power battery welding industry.To improve the efficiency of power battery welding and to solve the difficulty in selecting process parameters for meeting multiple objectives,a solution combining kernel ridge regression and Multi-Objective Particle Swarm Optimization (MOPSO) was adopted.In this scheme,the welding lower limit corresponding to process parameters was set,and the kernel ridge regression model was used for simulation based on Gauss kernel function.A set of process parameters was represented as a particle of MOPSO,and by means of a regression model,an optimal solution set for the specified welding objective was effectively obtained through three operational steps consisting of the evolution and variation of the population,the selection and optimization of the guide and the maintenance of solution set.In addition,K-nearest neighbor algorithm was referenced for designing an evaluation criterion to measure the reliability of each solution and further screen better solutions,which ensured that the selected process parameters own higher fault tolerance.The proposed method had solved the difficult problem in selecting process parameters faced by current power battery welding industry,which would enhance the efficiency and effectiveness of power battery welding.

Key words: power battery, laser welding, process parameters, kernel ridge regression, multi-objective particle swarm optimization