基于IECSDE算法的PEMFC改进分数阶子空间辨识模型

doi:10.19678/j.issn.1000-3428.0067981

摘要/Abstract

摘要： 为准确描述质子交换膜燃料电池(PEMFC)在其发电过程中的特性及变量影响关系,提出一种基于信息交流布谷鸟搜索差分进化(IECSDE)算法的改进分数阶子空间辨识方法来建立PEMFC分数阶模型。首先基于状态空间方程建立PEMFC模型,为了描述PEMFC的分数阶特性,将分数阶微分理论融入到模型中,引入Poisson滤波函数预处理实验数据,解决数据多阶不可导的问题,同时引入变步长记忆法处理分数阶微分时的权系数,提高子空间辨识精度。其次在辨识过程中的参数对于建模效果具有重大影响,因此基于IECSDE算法并对其进行优化,对布谷鸟搜索(CS)算法中的控制参数进行自适应处理,受到粒子群优化(PSO)算法的启发,改进随机游走方式提高收敛精度和速度,并引入差分进化(DE)算法与改进CS算法分别对种群进行优化,同时在寻优过程中进行信息交流提高种群的多样性和算法的鲁棒性。仿真结果表明,IECSDE算法的寻优能力在8种测试函数下比其他5种优化算法至少提升了10倍;通过对PEMFC测控平台收集到的实验数据进行模型辨识,所建立的模型将误差缩小到基于短记忆法的分数阶子空间辨识方法误差的20%,输出功率误差控制在0~0.1之间,输出电压误差控制在0~0.2之间,能够精准地模拟PEMFC发电过程。

关键词: 质子交换膜燃料电池, 分数阶子空间辨识, 变步长记忆法, 优化算法, 信息交流

Abstract: To accurately describe the characteristics of a Proton Exchange Membrane Fuel Cell (PEMFC) during its power-generation process and the relationship among variable effects, an improved fractional-order subspace identification method based on the Information Exchange Cuckoo Search Differential Evolution(IECSDE) algorithm is proposed to establish a fractional-order state-space model for the PEMFC. In this study, a PEMFC model is established based on state-space equations. Considering the fractional order of PEMFCs, fractional differential theory is integrated into the subspace identification algorithm, and a Poisson filter is utilized to address the multi-order derivability of the experimental data. Additionally, the variable-step memory method is introduced to manage the weight coefficients in fractional-order differentiation, thus improving the identification accuracy and reducing the computational complexity. The parameters used in the identification significantly affect the modeling. Therefore, the IECSDE algorithm is proposed to optimize the parameters. The control parameters in the Cuckoo Search(CS) algorithm are adaptively adjusted to optimize the convergence precision and speed. Inspired by the updating method in the particle swarm optimization algorithm, the random-walk method in the conventional CS algorithm is improved, thereby reducing its randomness. The population is optimized separately using the Differential Evolution(DE) algorithm and the improved CS algorithm, with information exchange performed during the optimization to enhance the population diversity and algorithm robustness. The simulation results indicate that the optimization capability of the IECSDE algorithm is at least 10 times higher than that of the other five optimization algorithms for eight test functions. The model identification uses experimental data obtained from the PEMFC measurement and control platform. The model established in this study affords an error reduction of 20% by employing the short memory-based fractional subspace identification error. Its output-power error is controlled between 0 and 0.1, and its output-voltage error is controlled between 0 and 0.2. Additionally, it accurately simulates the power generation of a PEMFC.

Key words: Proton Exchange Membrane Fuel Cell(PEMFC), fractional-order subspace identification, variable step memory method, optimization algorithm, information exchange

中图分类号:

TP391

秦灏, 戚志东, 于灵芝, 童新. 基于IECSDE算法的PEMFC改进分数阶子空间辨识模型[J]. 计算机工程, 2024, 50(6): 346-357.

QIN Hao, QI Zhidong, YU Lingzhi, TONG Xin. PEMFC Improved Fractional-order Subspace Identification Model Based on IECSDE Algorithm[J]. Computer Engineering, 2024, 50(6): 346-357.

https://www.ecice06.com/CN/Y2024/V50/I6/346

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