中国电机工程学报2012，Vol.32Issue(15)：155-161,7.
基于粒子群优化的超级电容器模型结构与参数辨识
Structure and Parameter Identification of Supercapacitors Based on Particle Swarm Optimization
摘要
Abstract
In order to analyze the dynamical characteristics of supercapacitor and predict its state of charge （SOC） and state of health （SOH） correctly, the model of supercapacitor should be built at first. On the basis of analyzing the characteristics of different modeling methods, a system identification method was presented to model the supercapacitor. Because the general error criterion cannot insure the minimal output error, the output error criterion was adopted and a corresponding nonlinear objective function was deduced. Then the particle swarm optimization algorithm was used to solve the problem, and then the model＇s parameters were obtained. With respect to the structure identification, the final output error （FOE） criterion was introduced. By comparing the value of FOE, the best order of the model was determined. The results of experiments and simulations show that the model presented in the paper can describe the supercapacitor＇s dynamic characteristics precisely and the modeling method is feasible and valid.关键词
超级电容器/建模/粒子群优化/结构辨识/参数/辨识/输出误差准则Key words
supercapacitor/optimization (PSO)/structureidentification/output error criterionmodeling/particle swarm identification/parameter分类
信息技术与安全科学引用本文复制引用
赵洋,韦莉,张逸成,孙家南..基于粒子群优化的超级电容器模型结构与参数辨识[J].中国电机工程学报,2012,32(15):155-161,7.基金项目
国家自然科学基金项目（50877054）. （）
