固体氧化物燃料电池（SOFC）是一种多输入多输出、强耦合和强非线性的新型发电装置，对其内部状态变量的预估将有助于了解实际SOFC的运行过程和实现高效控制器的设计。本文采用卡尔曼滤波算法对SOFC的状态进行预估。通过对SOFC发电原理的深入分析，建立其离散时间的状态空间模型；采用卡尔曼滤波算法对SOFC的各气体进气侧压力值进行预估，并将预估值带入输出电压方程，对SOFC下一时刻的电压进行预估。MATLAB/Simulink仿真结果表明，氢气、氧气和水蒸气压力的估计值与真实值的误差分别为0.425×105，0.141×105和0.364×105 Pa，远小于各气体压力测量值与真实值的误差1.479×105，1.165×105和1.155×105 Pa，同时SOFC输出电压的估计值较为符合真实值的变化，验证了卡尔曼滤波算法在SOFC状态预估中的有效性和实时性。

Solid oxide fuel cell (SOFC) is a new type of power generation device with multiple input and output,strong coupling and nonlinear.The estimation of internal state variables of SOFC will help to understand the operation process of practical SOFC and design the efficient controllers.In this paper,the state of SOFC is estimated by using Kalman filter algorithm.The discrete time state space model of SOFC is established through deep analysis of its power generation principle.The Kalman filter algorithm is used to estimate the pressure value of each gas inlet side of SOFC,and the estimated value is substituted into the output voltage equation to estimate the voltage of SOFC at the next moment.MATLAB/Simulink simulation results show that the errors between the estimated values and the real values of hydrogen,oxygen and water vapor pressures are 0.425×105,0.141×105 and 0.364×105 Pa respectively,which are far less than the errors between the measured values and the real values of each gas pressure of 1.479×105,1.165×105 and 1.155×105 Pa.At the same time,the estimated value of SOFC output voltage is more consistent with the change of the real value,which verifies the validity and realtime performance of Kalman filter algorithm in SOFC state estimation.

TK91

国家自然科学基金（5177070985）