基于改进遗传算法的PEMFC电堆稳态运行优化

在质子交换膜燃料电池(PEMFC)运行过程中,运行压力、反应气条件、质子膜含水状态、电堆温度、输出电流等因素,都会影响电堆功率输出,因此须确定电堆输出最大功率的最优运行状态。为确定最大输出功率,建立了一个PEM-FC电堆参数模型和优化目标,并提出一种实现此优化的改进遗传算法。使用1kW电堆的实验数据通过最小二乘法确定了模型参数,同时详细叙述了如何利用此遗传算法实现优化,对PEMFC电堆最优运行条件进行了分析。计算和分析结果表明此遗传算法能够有效确定最大输出功率和最优运行状态。

Optimization of steady-state operation for a PEMFC stack model with a modified genetic algorithm

Generally,because many factors such as operating pressures,gas conditioning,membrane hydration state,temperature,demanded current etc.,affect the output power of PEMFC stack,it is necessary to determine the optimal operating conditions for the maximum output power during the Proton Exchange Membrane Fuel Cell(PEMFC)stack operation.In order to determine the maximum output power,a steady-state PEMFC stack model and an optimization object suitable for this purpose are developed in this paper and then a modified genetic algorithm(MGA)for this optimization is presented.A set of experimental data of 1 kW class PEMFC stack are used to determine the optimal model parameters which let the model fit the data well by least squares algorithm.It is also illustrated that this genetic algorithm is applied to solve this optimization object and analyze the optimal operating conditions in detail.This genetic algorithm provides an effective method for the determination of the maximum output power and the optimal operation.