关键词: 蚁群算法, 支持向量机, 参数优化, 锂离子电池, 最优充电

Abstract: To solve the key problem of charging current setting in optimal charging, ant colony algorithm optimizing core parameters of regression support vector machine are put forward and used in optimal charging current forecast. Core parameters C and g of SVR are in the form of node values in the ACO. This paper uses errors under the condition of cross validation as the objective function to update pheromone concentration, to make the SVR optimal performance. According to the lithium ion battery charging data, the optimal charging current ACO-SVR model is established. The results show that the ACO-SVR model shows less optimization time and better prediction accuracy. Through the theoretical analysis and experimental results, it attests that this method is practical and effective.

Key words: ant colony algorithm, support vector machine, parameter optimization, lithium ion batteries, optimal charging