(631d) Real-Time Monitoring and Control of Lab-Scale Hydrogen Energy Systems: An Experimental and Modeling Approach

Yuanxing Liu^1,2,3, Sahithi Srijana Akundi^1,2,3, Austin Braniff⁴, Beatriz Dantas⁴, Yuhe Tian⁴, Shayan S. Niknezhad¹, Faisal Khan^2,3*, Efstratios N. Pistikopoulos^1,3*

¹Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA

²Mary Kay O’Connor Process Safety Center (MKOPSC), Texas A&M University, College Station, TX, USA

³Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA

⁴Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA

*Presenter E-mail: liuyuanxing@tamu.edu

Correspondence: fikhan@tamu.edu and stratos@tamu.edu

2024 AIChE Annual Meeting

October 27-31, 2024, San Diego, CA

Abstract

The advent of fuel cell electric vehicles (FCEVs) marks a significant milestone in the quest for sustainable transportation solutions, driving the need for advanced hydrogen energy systems that are both scalable and efficient. As the world grapples with the challenges of climate change and the urgent need for decarbonization, hydrogen emerges as a critical component of the future energy matrix, offering a viable pathway to reducing greenhouse gas emissions across various sectors. Within this context, the development of comprehensive hydrogen energy cycles tailored to FCEV applications is paramount. This research endeavor aims to address these needs by delving into an innovative examination of lab-scale hydrogen energy loops, integrating experimental setups with modeling techniques to replicate the entire spectrum of hydrogen production, storage, and utilization processes.

At the core of our investigation lies a downscaled experimental framework, featuring a proton exchange membrane (PEM) water electrolyzer for the generation of hydrogen, metal hydride storage units for its compact and safe storage, and a PEM fuel cell for the efficient conversion of hydrogen into electricity, thus encapsulating the full hydrogen energy cycle for FCEV operations. This setup not only provides a microcosm of the broader hydrogen economy but also serves as a testbed for the exploration and optimization of hydrogen systems at a manageable scale.

To navigate the complexities and safety challenges inherent in hydrogen systems, we introduce a Real-Time Risk-Based Optimization Framework (RTRBOF), utilizing multi-parametric programming to craft an explicit model predictive control (eMPC) policy designed for these lab-scale prototypes. This initiative began with the development of high-fidelity models, calibrated with parameters extracted from experimental observations. These models were refined and streamlined, enabling the formulation of optimization challenges that incorporate safety measures, including precautions against thermal runaway, into a cohesive decision-making framework. The culmination of this process is the realization of an eMPC policy, integrated into a microcontroller for real-time application, facilitating the dynamic modulation of operational parameters to enhance the efficiency, safety, and reliability of the hydrogen system.

Experimental validations serve to underscore the effectiveness of our proposed control strategy, highlighting its capacity for real-time optimization of the hydrogen production-storage-utilization continuum. This capability is instrumental in addressing the challenges posed by the miniaturization of safety-critical hydrogen systems, a crucial step toward the realization of scalable and efficient hydrogen energy systems for the automotive sector.

This work contributes to the field of monitoring and control technologies within the hydrogen energy domain but also lays a foundation for future innovations of scalable, efficient, and safe hydrogen energy systems.

Keywords: Real-time monitoring and control, hydrogen energy systems, multi-parametric programming, process safety management, experimental operation