基于遗传算法优化最小二乘支持向量回归机的平板型固体氧化物燃料电池的控制相关动态辨识建模

研究目的：为了同时预测固体氧化物燃料电池（SOFC）的电压、温度动态特性和设计控制器,建立SOFC的控制相关动态辨识模型。创新要点：为了建立SOFC更精确的最小二乘支持向量回归机（LSSVR）动态模型,采用遗传算法（GA）优化LSSVR的参数。所建GA-LSSVR模型可同时预测SOFC的电压和温度动态特性。研究方法：1.分析SOFC的电化学和能量平衡子模型。2.利用所选择的最优LSSVR参数,建立了SOFC的GA-LSSVR动态辨识模型。通过仿真分析和比较,验证了所建模型的有效性（图3和4）。3.利用所建模型的预测结果,与模拟退火算法优化最小二乘支持向量回归机（SAA-LSSVR）和5折交叉验证最小二乘支持向量回归机（5FCV-LSSVR）模型的预测结果进行了比较,表明所建立的GA-LSSVR模型具有较高的预测精度（表3和4）。重要结论：通过比较SAA-LSSVR和5FCV-LSSVR模型的预测结果,发现所建GA-LSSVR模型具有较好的预测性能和精度。基于所建立的GA-LSSVR模型可进行有效的多变量控制器设计。

Control-oriented dynamic identification modeling of a planar SOFC stack based on genetic algorithm-least squares support vector regression

For predicting the voltage and temperature dynamics synchronously and designing a controller, a control-oriented dynamic modeling study of the solid oxide fuel cell（SOFC） derived from physical conservation laws is reported, which considers both the electrochemical and thermal aspects of the SOFC. Here, the least squares support vector regression（LSSVR） is employed to model the nonlinear dynamic characteristics of the SOFC. In addition, a genetic algorithm（GA）, through comparing a simulated annealing algorithm（SAA） with a 5-fold cross-validation（5FCV） method, is preferably chosen to optimize the LSSVR＇s parameters. The validity of the proposed LSSVR with GA（GA-LSSVR） model is verified by comparing the results with those obtained from the physical model. Simulation studies further indicate that the GA-LSSVR model has a higher modeling accuracy than the LSSVR with SAA（SAA-LSSVR） and the LSSVR with 5FCV（5FCV-LSSVR） models in predicting the voltage and temperature transient behaviors of the SOFC. Furthermore, the convergence speed of the GA-LSSVR model is relatively fast. The availability of this GA-LSSVR identification model can aid in evaluating the dynamic performance of the SOFC under different conditions and can be used for designing valid multivariable control schemes.