Fuel Cell Characterization System

The Fuel Cell Characterization System is an advanced experimentation and research platform for studying PEM fuel cells. It enables detailed exploration of efficiency, durability, and performance under varied operating conditions. The setup includes five single cells and three additional MEA sizes, allowing comparative analysis and scale-up research. Its stackable architecture links single-cell studies to stack-level applications, bridging the gap between laboratory learning and practical deployment. With PC-enabled control, researchers can regulate temperature, humidity, and gas pressure while receiving real-time feedback. A programmable electronic load supports experiments on dynamic operating conditions and optimization. Integrated humidifiers and gas management systems ensure consistent performance. Built-in safety features such as hydrogen leak detection, automatic purging, and overpressure protection guarantee secure operations. Tailored for universities, training centers, and R&D labs, the system provides a complete, safe, and flexible environment for advancing green hydrogen research and innovation.

Key Features

Multi-Cell Configuration:
Five single cells and three additional cells of varying MEA sizes (20 cm², 30 cm², 50 cm²) allow flexible experimentation and comparison across scales.
Stackable Design:
Enables research on scaling effects by extending studies from single cells to stack configurations, simulating real-world deployment scenarios.
Precise Temperature Control:
Mica film-based heaters and dedicated PID controllers ensure accurate thermal management, essential for optimizing efficiency and durability.
Advanced Humidification System:
Dual-channel Nafion membrane humidifiers for hydrogen and oxygen provide controlled humidity (40–70°C) for improved electrochemical performance.
Programmable Electronic Load:
500 W DC load with CC, CV, CR, and CP modes simulates different operating conditions, enabling detailed characterization of power output and transient responses.
Integrated Instrumentation:
Mass flow controllers (0–1 LPM), RTD temperature sensors, and capacitive humidity sensors deliver accurate, real-time data for deeper research insights.
Gas Management & Safety:
Automatic purge unit, overpressure relief valve, hydrogen leak detectors, and solenoid valves ensure reliable and safe experimentation.
PC-Based Automation & Data Logging:
RS-485, MODBUS, and USB connectivity support real-time monitoring, remote control, and automated data acquisition for streamlined research workflows.
Flexible Hydrogen Supply:
Compatible with electrolyzer-fed hydrogen and standard regulators, allowing integration with renewable hydrogen generation systems.
Research-Focused Architecture:
Designed for investigating efficiency optimization, humidification strategies, durability testing, thermal management, and hybrid integration with batteries or supercapacitors.

Learning Module

Fuel Cell Performance & Characterization

Study polarization curves to analyze activation, ohmic, and mass transport losses.
Measure power density and efficiency across different temperatures, pressures, and loads.
Compare performance of single cells vs. stackable configurations for real-world scaling studies.

Gas Flow, Humidity & Pressure Studies

Conduct experiments with dry vs. humidified hydrogen and oxygen for efficiency optimization.
Explore the effects of varying gas pressures on stability, efficiency, and power output.
Investigate purge cycle intervals and their impact on long-term fuel cell durability.
Compare performance across different MEA sizes for scale-based learning.

Safety, Durability & Advanced Applications

Validate system safety through hydrogen leak detection, automatic shutdown, and purge protocols.
Perform long-term durability studies under cyclic load and environmental variations.
Integrate with electrolyzer-fed hydrogen for renewable-to-electricity pathways.
Explore hybrid system applications by combining fuel cells with batteries or supercapacitors for load leveling.
Develop and test advanced PC-based control algorithms for optimization and real-time monitoring.

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