摘要
高效可靠的控制单元是燃料电池高性能、长寿命工作的关键,而一个可以准确描述燃料电池动态特性的系统模型则是控制策略实施的前提。针对燃料电池系统的空气供给系统,介绍了一种由阴极腔体和各辅助组件组成的集总参数动态模型,基于工况数据利用非线性最小二乘法对模型进行参数辨识;同时,建立燃料电池输出电压模型,采用混合遗传粒子群优化算法辨识电压模型参数,最终实验证明模型稳态输出误差在4%以内,可满足在线反馈控制应用。
An efficient and reliable control unit is the key to high performance and long life of the fuel cell,and a system model that can accurately describe the dynamic characteristics of the fuel cell is the prerequisite of control strategy implementation.For the air supply system of the fuel cell system,this paper introduces a lumped parameter dynamic model consisting of the cathode cavity and various auxiliary components.The model parameters are identified by the non-linear least square method based on the operating condition data.At the same time,the output voltage model of the fuel cell is established,and the parameters of the voltage model are identified with the hybrid genetic particle swarm optimization algorithm.The final experiment shows that the steady-state output error of the model is within 4%,which can satisfy the application of online feedback control.
出处
《机电一体化》
2020年第3期18-26,共9页
Mechatronics
基金
中国国家重点研发计划(课题编号:2018YFB0106502)
上海市科学技术委员会(课题编号:18DZ1101103)。
关键词
质子交换膜燃料电池系统模型
参数辨识
混合遗传粒子群优化算法
proton exchange membrane fuel cell system model
parameter identification
hybrid genetic particle swarm optimization algorithm
