Abstract: Proton exchange membrane (PEM) fuel cells are electrochemical devices that covert the chemical energy to electricity directly.This study was concentrated on the air supply system.This paper presented an improved four order fuel cell air supply system model,analyzing its constraints.And the linear parameter varying (LPV) model was proposed to deal with the non-linear characteristics of the dynamic model.To cope with the problem of immeasurable states,the Kalman filter was used to estimate the states and the output variables which should be measured were selected.Additionally,the state space model predictive control algorithm satisfying the constraints is designed based on LPV model to control the compressor voltage to ensure an adequate response to hydrogen fuel.Simulation results showed that model predictive controller based on LPV model is able to effectively control the fuel cell air system,and to meet the compressor surge and blocking boundary conditions and other constraints,Comparing with single model predictive control,better control performance was obtained with the proposed LPV-MPC.

Key words: fuel cells, linear parameter varying model, model-predictive control, dynamic modeling, control

摘要： 质子交换膜燃料电池是一种通过氢气和氧气的电化学反应将化学能直接转化为电能的装置。提出一种改进的四阶燃料电池进气系统模型，分析了系统的约束性。针对系统模型所具有的非线性特性，提出建立线性变参数（LPV）模型用于对系统的控制。针对状态变量不可测的问题引入卡尔曼滤波器，同时通过可观性分析得出系统所需测量的最佳变量。在符合约束条件下设计基于线性变参数模型的状态空间模型预测控制器，控制空压机的工作电压保证氢气燃料的充分反应。仿真结果表明，基于LPV模型的模型预测控制器能够对空气进气系统进行有效的控制，且满足空压机喘振和阻塞边界等约束条件，与单模型预测控制相比具有更好的控制效果。

关键词: 燃料电池, 线性变参数模型, 模型预测控制, 动态建模, 控制