摘要
为了分析超级电容器的动态特性,并准确估计荷电状态、健康状态等信息,需要建立超级电容器模型。提出以系统辨识方法作为建模手段,为克服广义误差准则不能保证模型输出误差最小的缺点,采用输出误差准则并推导出对应的非线性目标函数。应用粒子群优化算法对该目标函数进行优化求解,并获得模型参数。针对结构辨识问题,采用最终输出误差准则计算结构判别指标,通过比较该指标值确定模型最佳阶次。实验和仿真结果表明,所提模型能精确描述超级电容器的动态特性,建模方法可行。
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.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2012年第15期155-161,2,共7页
Proceedings of the CSEE
基金
国家自然科学基金项目(50877054)~~
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
supercapacitor
optimization (PSO)
structureidentification
output error criterionmodeling
particle swarm identification
parameter