基于经济模型预测控制的燃料电池空气管理

Economic model predictive control based on air management in fuel cells

针对质子交换膜燃料电池(PEMFC)的空气供给问题,提出一种经济模型预测控制(EMPC)的算法。区别于传统空气供给管理算法通过跟踪过氧比来调节系统的性能,提出的EMPC控制器通过构建与PEMFC的净输出功率相关的经济效益函数,作为优化问题的成本函数,旨在使PEMFC系统在任何工况运行时的净输出功率最大化,并通过仿真验证控制算法的稳定性和有效性。仿真结果表明,与动态过氧比和定值过氧比的模型预测控制器相比,EMPC控制器在暂态过程中的净输出功率分别提高了2.93%和3.82%,在稳态过程中的净输出功率分别提高了0.94%和0.67%。

An algorithm for economic model predictive control(EMPC)was proposed to address the issue of air supply in proton exchange membrane fuel cell(PEMFC).In contrast to conventional algorithms for air supply management that adjusted system performance by tracking the peroxide ratio,the proposed EMPC controller constructed an economic benefit function correlated with the net output power of the PEMFC.This function was served as the cost function for the optimization problem.The objective was to maximize the net output power of the PEMFC system under any operating condition.The stability and effectiveness of the control algorithm were validated through simulation.The simulation results revealed that,when compared to model predictive controllers utilizing dynamic peroxide ratios and fixed peroxide ratios,the EMPC controller increased net output power by 2.93%and 3.82%,respectively,during transient processes,and by 0.94%and 0.67%,respectively,during steady-state processes.