基于粒子群优化的超级电容器模型结构与参数辨识-中国电机工程学报.pdf

第32 卷 第15 期 中 国 电 机 工 程 学 报 Vol.32 No.15 May 25, 2012 2012 年5 月25 日 Proceedings of the CSEE ©2012 Chin.Soc.for Elec.Eng. 155 文章编号：0258-8013 (2012) 15-0155-07 中图分类号：TM 53 文献标志码：A 学科分类号：470·40 基于粒子群优化的 超级电容器模型结构与参数辨识 赵洋，韦莉，张逸成，孙家南 ( 同济大学电子与信息工程学院，上海市 嘉定区 201804) Structure and Parameter Identification of Supercapacitors Based on Particle Swarm Optimization ZHAO Yang, WEI Li, ZHANG Yicheng, SUN Jianan (College of Electronic and Information Engineering, Tongji University, Jiading District, Shanghai 201804, China) ABSTRACT: In order to analyze the dynamical characteristics 电容器的动态特性，建模方法可行。 of supercapacitor and predict its state of charge (SOC) and state 关键词：超级电容器；建模；粒子群优化；结构辨识；参数 of health (SOH) correctly, the model of supercapacitor should 辨识；输出误差准则 be built at first. On the basis of analyzing the characteristics of different modeling methods, a system identification method 0 引言 was presented to model the supercapacitor. Because the general error criterion cannot insure the minimal output error, the 随着一次能源储量的日渐缩小，太阳能和风能 output error criterion was adopted and a corresponding 等清洁能源发电技术得到世界各国的高度重视。在 nonlinear objective function was deduced. Then the particle 此类非恒定能源供给系统中，为保证供电的安全性 swarm optimization algorithm was used to solve the problem, 和可靠性需要先进的电能存储技术作为支撑[1] 。超 and then the model’s parameters were obtained. With respect to 级电容器是一种工作机理有别于蓄电池和传统电 the structure identification, the final output error (FOE) criterion was introdu