燃料电池空气供给系统分层控制：一种效率感知的MIMO控制方法结合新型约束执行参考治理器
Hierarchical Fuel-Cell Airpath Control: an Efficiency-Aware MIMO Control Approach Combined with a Novel Constraint-Enforcing Reference Governor
AI总结：
针对PEMFC空气供给系统，提出分层MIMO内模控制结合新型级联截面参考治理器，实现流量压力跟踪、效率优化与OER约束，较SISO降低氢耗7.36%，较传统负载治理器改善功率跟踪误差与最坏OER。
摘要：
本文针对质子交换膜燃料电池（PEMFC）系统的空气供给系统，提出了一种分层多变量控制与约束管理方法。控制目标包括跟踪期望的压缩机质量流量和阴极入口压力、维持最小氧气过剩比（OER），并使系统以最大净效率运行。设计并仿真了一个多输入多输出（MIMO）内模控制器（IMC）来跟踪流量和压力设定点，尽管被控对象动态强耦合，该控制器仍表现出高性能。生成了一种新的设定点映射，用于根据电堆电流负载计算最高效的阴极入口压力。为强制执行OER约束，开发并测试了一种新型参考治理器（RG），该治理器具有管理多个参考信号的能力（级联RG）以及加速和减速参考信号的能力（截面RG）。与单输入单输出（SISO）空气流量控制方法相比，所提出的MIMO控制方法显示出氢燃料消耗最多降低7.36%。与传统负载治理器相比，新型级联截面参考治理器（CC-RG）在净功率跟踪上的平均绝对百分比误差（MAPE）最多降低3.68%，并在实际驾驶循环仿真中显著改善了最坏情况下的OER。控制开发和验证在两个燃料电池系统（FCS）模型上进行：一个非线性开源模型和一个专有的福特高保真模型。
英文摘要：
This paper presents a hierarchical multivariable control and constraint management approach for an air supply system for a proton exchange membrane fuel cell (PEMFC) system. The control objectives are to track desired compressor mass airflow and cathode inlet pressure, maintain a minimum oxygen excess ratio (OER), and run the system at maximum net efficiency. A multi-input multi-output (MIMO) internal model controller (IMC) is designed and simulated to track flow and pressure set-points, which showed high performance despite strongly coupled plant dynamics. A new set-point map is generated to compute the most efficient cathode inlet pressure from the stack current load. To enforce OER constraints, a novel reference governor (RG) with the ability to govern multiple references (the cascade RG) and the ability to speed up as well as slow down a reference signal (the cross-section RG) is developed and tested. Compared with a single-input single-output (SISO) air-flow control approach, the proposed MIMO control approach shows up to 7.36 percent lower hydrogen fuel consumption. Compared to a traditional load governor, the novel cascaded cross-section RG (CC-RG) shows up to 3.68 percent less mean absolute percent error (MAPE) on net power tracking and greatly improved worst-case OER on realistic drive-cycle simulations. Control development and validations were conducted on two fuel cell system (FCS) models, a nonlinear open-source model and a proprietary Ford high-fidelity model
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
