Parameter fitting of PEMFC models based on adaptive differential evolution
Parameter fitting of PEMFC models based on adaptive differential evolution
复制标题
基于自适应差分进化的PEMFC模型参数拟合
DOI：
10.1016/j.ijepes.2014.04.043
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发表时间：
2014
影响因子：
5.2
通讯作者：
Gexiang Zhang
中科院分区：
文献类型：
--
作者：
Jixiang Cheng;Gexiang Zhang
质子交换膜燃料电池 (PEMFC) 的优化建模对于理解和解释整个电池行为极其重要。由于模型参数的确定是一个复杂的优化问题，智能优化技术是对质子交换膜燃料电池进行优化建模的有效策略。本文提出了一种自适应差分进化算法（ADE）来精确拟合PEMFC模型的参数。主要贡献是提出了ADE对PEMFC模型参数拟合的建议。为了避免过早收敛并提高搜索效率，ADE 使用两种自适应方案来动态调整其变分算子和控制参数。通过应用 ADE 求解基准函数并使用无噪声或噪声数据提取四个 PEMFC 的模型参数，验证了 ADE 的有效性和鲁棒性。实验结果表明，该方法比其他几种算法产生更准确、更稳健的模型，并且 ADE 获得的极化曲线在所有情况下都与实验数据吻合良好。这项工作表明 ADE 是解决 PEMFC 优化建模问题的有效替代方案。
Optimal modeling of proton exchange membrane fuel cell (PEMFC) is extremely important for the understanding and interpretation of the overall cell behavior. Intelligent optimization technique is an effective strategy to optimally model PEMFCs because the determination of model parameters is a complex optimization problem. In this paper, an adaptive differential evolution algorithm (ADE) is proposed to accurately fit parameters of PEMFC models. The main contribution is the proposal of ADE for the parameter fitting of PEMFC models. To avoid premature convergence and increase search efficiency, ADE uses two adaptation schemes to dynamically adjust its variation operators and control parameters. The effectiveness and robustness of ADE is verified by applying it to solve benchmark functions and to extract model parameters of four PEMFCs using both noise-free or noised data. Experimental results demonstrate that the proposed method produces more accurate and robust models than several other algorithms and the polarization curves obtained by ADE match well with the experimental data in all cases. This work indicates that ADE is an effective alternative to solve the PEMFC optimal modeling problem.