Segmented Model-Based Hydrogen Delivery Control for Proton Exchange Membrane Fuel Cells Using a Port-Hamiltonian Approach

Core Concepts

This paper proposes an extended interconnection and damping assignment passivity-based control technique (IDA-PBC) to control the pressure dynamics in the fuel delivery subsystem (FDS) of proton exchange membrane fuel cells.

Abstract

The key highlights and insights of this content are: The fuel cell stack is modeled as a distributed parameter system using partial differential equations (PDEs). The segmentation concept is used to approximate the PDEs model by ordinary differential equations (ODEs) model, where each segment has multiple ODEs to obtain the lumped-sum model. A generalized multi-input multi-output (MIMO) lumped parameters model is developed in the port-Hamiltonian framework based on mass balance to minimize the modeling error. The modeling errors arise due to the difference between spatially distributed pressures in FDS segments, and the difference between the actual stack pressure and the measured output pressure of the anode. The passivity of each segment is maintained, and the port-Hamiltonian structure of each segment is preserved, ensuring the interconnection feasibility of the segments. An extended energy-shaping and output tracking IDA-PBC based state-feedback controller is proposed to control the spatially distributed pressure dynamics in the segmented anode. A sliding mode observer of high order is designed to estimate the unmeasurable pressures in FDS with known disturbances. The performance recovery of output feedback control is accomplished with explicit stability analysis. The effectiveness of the proposed IDA-PBC approach is validated through simulation results.

Stats

The paper provides the following key figures and metrics: The area of the anode inlet orifices (Aai) is 8.04e-06 m^2. The area of the bleed orifice (Abd) is 7.24e-05 m^2. The area of the cathode orifices (Aor) is 7.24e-06 m^2. The diameter of the blower blade (dbl) is 0.2286 m. The Faraday constant (F) is 96485 C/s.

Quotes

"The segmentation technique were used to address the problems of water management, the power management, the fault tolerance, the life cycle, and the thermal management for the PEM fuel cells." "The port-Hamiltonian (pH) based control design technique is developed in recent decade which in particular can be used as a geometrical framework to model the physical systems as a network model through the Dirac structure."

Key Insights Distilled From

Segmented Model-Based Hydrogen Delivery Control for PEM Fuel Cells: a Port-Hamiltonian Approach

by Lalitesh Kum... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11959.pdf

Deeper Inquiries

How can the segmentation concept be extended to other types of fuel cells beyond PEM fuel cells

The segmentation concept utilized in the context of PEM fuel cells can be extended to other types of fuel cells by considering the specific characteristics and dynamics of each type. For example, in solid oxide fuel cells (SOFCs), which operate at high temperatures and have different material properties, the segmentation concept can be applied to model the spatial distribution of temperature, gas concentrations, and electrochemical reactions within the fuel cell stack. By dividing the fuel cell stack into segments and approximating the partial differential equations with ordinary differential equations, a segmented model can be developed to capture the distributed parameters of the SOFC system. This segmentation approach can help in optimizing the control strategies for SOFCs, similar to the approach proposed for PEM fuel cells in the context provided.

What are the potential challenges in implementing the proposed IDA-PBC controller in a real-world PEM fuel cell system

Implementing the proposed IDA-PBC controller in a real-world PEM fuel cell system may face several challenges. Some of the potential challenges include: Modeling Accuracy: The accuracy of the segmented model and the lumped parameter model developed based on the segmentation concept may impact the performance of the controller. Ensuring that the segmented model adequately represents the dynamics of the fuel cell system is crucial for effective control. Sensor Placement: The availability and placement of sensors to measure the required variables, such as pressure dynamics in the fuel delivery subsystem, can be challenging in a real-world system. Ensuring accurate and timely sensor data is essential for the controller's performance. Controller Tuning: Tuning the controller parameters, such as the gains in the IDA-PBC approach, to achieve the desired control objectives while maintaining stability can be a complex task. The controller design may need to consider the nonlinearities and uncertainties in the fuel cell system. System Complexity: Real-world fuel cell systems may have additional complexities, such as varying operating conditions, external disturbances, and component degradation over time. Adapting the controller to handle these complexities and ensuring robust performance is essential.

How can the proposed approach be adapted to handle uncertainties and disturbances in the fuel cell system that were not considered in this work

To adapt the proposed approach to handle uncertainties and disturbances in the fuel cell system, several strategies can be employed: Robust Control Techniques: Implementing robust control techniques, such as H-infinity control or sliding mode control, can help the controller handle uncertainties and disturbances effectively. These techniques provide robust performance in the presence of model uncertainties and external disturbances. Adaptive Control: Introducing adaptive control strategies that can adjust the controller parameters based on the system's changing dynamics and uncertainties can enhance the controller's performance. Adaptive control algorithms can continuously update the controller to maintain optimal operation. Fault Detection and Isolation: Incorporating fault detection and isolation mechanisms in the control system can help identify and mitigate disturbances or faults in the fuel cell system. By detecting anomalies early, the controller can adapt its strategies to ensure system stability. Kalman Filtering: Utilizing Kalman filtering or other estimation techniques can improve the accuracy of state estimation and reduce the impact of measurement noise and uncertainties. By incorporating estimation algorithms, the controller can make more informed decisions in the presence of uncertainties. By integrating these strategies into the proposed IDA-PBC approach, the controller can be enhanced to handle uncertainties and disturbances in the fuel cell system effectively.