Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model.

Area of Science:

• Energy Systems Engineering
• Computational Science
• Electrochemistry

Background:

• Proton Exchange Membrane Fuel Cells (PEMFCs) are crucial for sustainable energy, but their complex dynamics necessitate accurate performance modeling.
• Existing modeling techniques struggle with the nonlinear behavior inherent in PEMFCs, limiting efficiency optimization.

Purpose of the Study:

• To develop a novel reinforcement learning (RL) approach for precise parameter estimation in PEMFCs.
• To address the challenges posed by the nonlinear and complex dynamics of PEMFC systems.
• To enhance the accuracy and adaptability of PEMFC performance models.

Main Methods:

• A reinforcement learning (RL) algorithm was designed to minimize the sum of squared errors between measured and simulated PEMFC voltages.
• The RL-based estimation method was developed to be adaptive and self-improving, learning from continuous system feedback.
• The approach was validated using experimental data from commercial PEMFCs, including Temasek 1 kW, Nedstack PS6 (6 kW), and Horizon H-12 (12 W).

Main Results:

• The proposed RL-based method demonstrated superior accuracy and performance in PEMFC parameter estimation compared to traditional metaheuristic techniques.
• Continuous learning from system feedback enabled an adaptive and self-improving estimation process.
• Validation through theoretical and experimental comparisons confirmed the effectiveness of the RL approach.

Conclusions:

• The novel RL approach provides a highly accurate and adaptive method for modeling PEMFC performance.
• This research contributes to improving PEMFC efficiency and advancing the adoption of hydrogen-based energy solutions.
• Precise PEMFC modeling is essential for unlocking their full potential in sustainable energy applications.