| 基于改进粒子群算法的超级电容参数辨识 |
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| 引用本文： | 任春明. 基于改进粒子群算法的超级电容参数辨识[J]. 工业控制计算机, 2020, 0(2): 63-67 |
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| 作者姓名： | 任春明 |
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| 作者单位： | 上海大学微电子开发与研究中心 |
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| 摘 要： | 针对超级电容的模型参数辨识不准确问题,首先分析了超级电容单体的储能原理和性能特点,将二分支等效模型作为超级电容的模型,然后使用最小二乘算法和改进粒子群算法对模型参数进行辨识,最后通过仿真和实验比较两种算法辨识效果,证实该文所提出的改进粒子群算法更能准确地辨识出超级电容模型参数。
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| 关 键 词： | 粒子群算法 超级电容 参数辨识 |
An Improved Particle Swarm Optimization Based Super Capacitor Parameter Identification |
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| Abstract: | In order to solve the problem of the inaccuracy of model parameter identification of super capacitor,this paper first analyzes the energy storage principle and performance characteristics of super capacitor unit,and then takes the two branch equivalent model as the model of super capacitor.The least square algorithm and the improved particle swarm optimization algorithm are used to recognize the model parameters.Finally,comparison between the least square algorithm and the improved particle swarm optimization algorithm is validated by the simulation and experiment.The results show that the improved particle swarm optimization algorithm can identify the parameters of the super capacitor model more accurately. |
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| Keywords: | particle swarm optimization super capacitor parameter identification |
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