CAJ | 학술논문

针对一辆小型燃料电池电动车的2 kW质子交换膜燃料电池（ PEMFC）动力系统，利用遗传算法优化的BP神经网络建立其电压输出特性模型，将PEMFC部分实测数据作为遗传算法优化的BP神经网络的训练样本对其进行训练，利用训练好的神经网络对电堆电压输出特性进行预测，并与实验数据进行对比，结果显示：网络预测的输出电压与实测输出电压之间的最大相对误差均保持在4%之内。
침대일량소형연료전지전동차적2 kW질자교환막연료전지（ PEMFC）동력계통，이용유전산법우화적BP신경망락건립기전압수출특성모형，장PEMFC부분실측수거작위유전산법우화적BP신경망락적훈련양본대기진행훈련，이용훈련호적신경망락대전퇴전압수출특성진행예측，병여실험수거진행대비，결과현시：망락예측적수출전압여실측수출전압지간적최대상대오차균보지재4%지내。
For a 2 kW PEMFC stack power system of a lightweight electric vehicle,using BP neural network which have be optimized by genetic algorithm,the model of the characteristic of voltage output of the stack is established, and part of the measured data of the PEMFC is taken as the training samples of the BP neural network that be opti-mized by genetic algorithm to train the network. Then using the trained neural network model,the output voltage of the system is predicted and compared with the test data. The result show that the maximum relative errors between the network predicted voltage and the measured output voltage are keep in 4%.