Comprehensive Dynamic Modeling and Constraint-Aware Air Supply Control for Localized Water Management in Automotive Polymer Electrolyte Membrane Fuel Cells

TL;DR

This work develops a comprehensive nonlinear model of an automotive PEMFC system that couples a P2D stack with balance-of-plant dynamics to capture local hydration and thermal phenomena. It introduces a reduced-order, control-oriented hydration model and a predictive constraint-aware command governor to enforce membrane hydration at the anode inlet during transients, prioritizing fast air-supply responses while accounting for surge and choke constraints. The air-supply LQI controller, cooling-tracking, and power-tracking strategies are validated on a high-fidelity Ford model across drive cycles, showing effective mitigation of hydration violations without sacrificing power performance. The approach advances durable, high-performance automotive PEMFC operation by enabling localized water management through fast, predictive, constraint-aware control.

Abstract

In this paper, a predictive constraint-aware control scheme is formulated within the Command Governor (CG) framework for localized hydration management of a proton exchange membrane (PEM) fuel cell system. First, a comprehensive nonlinear dynamic model of the fuel cell system is presented which includes a pseudo 2-dimensional (P2D) model of the stack, reactant supply and cooling subsystems. The model captures the couplings among the various subsystems and serves as the basis for designing output feedback controllers to track the optimal set-points of the air supply and cooling systems for power optimization. The closed-loop nonlinear model is then used to analyze the dynamic behavior of membrane hydration near the anode inlet, the driest region of the membrane in a counter-flow configuration, under various operating conditions. A reduced-order linearized model is then derived to approximate hydration behavior with sufficient fidelity for constraint enforcement. This model is used within the CG framework to adjust the air supply set-points when necessary to prevent membrane dry-out. The effectiveness of the proposed approach in maintaining local membrane hydration while closely tracking the requested net power is demonstrated through realistic drive-cycle simulations.

Figures (16)

• Figure 1: Block diagram of the proposed constraint management strategy for localized water management. $I_\text{d}$ is the current demand input. $r_\text{a}$ and $r_\text{c}$ denote the desired set-points for the air and coolant supply subsystems, while $v_\text{a}$ is the adjusted set-point provided to the air supply controller. $u_\text{a}$ and $u_\text{c}$ are the control actions applied to the subsystems, and $y_\text{a}$ and $y_\text{c}$ denote the corresponding subsystem outputs. $\hat{x}$ denotes the internal states associated with membrane hydration, estimated by an observer from $y_\text{m}$.
• Figure 2: Schematic diagram of the fuel cell system. All volumes and manifolds with associated dynamic states are identified by double lines. Single lines indicate direct connections between components without intermediate volumes. The red section represents the anode-side BoP components, the purple section corresponds to the cathode-side BoP components, and the blue section illustrates the cooling system.
• Figure 3: Schematic of a shell-and-tube type gas-to-gas membrane humidifier.
• Figure 4: Schematic of the pseudo-two-dimensional (P2D) fuel cell model in counter-flow configuration, showing discretization along the channel direction into control volumes (CVs). The layers include: BPa (anode bipolar plate), GCa (anode gas channel), GDLa (anode gas diffusion layer), MPLa (anode microporous layer), CLa (anode catalyst layer), Mb (membrane), CLc (cathode catalyst layer), MPLc (cathode microporous layer), GDLc (cathode gas diffusion layer), GCc (cathode gas channel), and BPc (cathode bipolar plate).
• Figure 5: Block diagram of the control architecture for the automotive FCS.