Research on Cold Start of Proton-Exchange Membrane Fuel Cells Based on Model Predictive Control.

Area of Science:

• * Fuel Cell Technology
• * Electrochemistry
• * Materials Science

Background:

• * Cold start limitations hinder fuel cell adoption due to water freezing.
• * Ice formation can damage the fuel cell stack by altering pore structure.
• * Effective startup strategies are crucial for durability and performance.

Purpose of the Study:

• * To analyze the impact of startup strategies on fuel cell stack temperature and ice formation.
• * To develop and validate a lumped parameter cold-start model.
• * To implement and evaluate a model predictive control (MPC) algorithm for cold start optimization.

Main Methods:

• * Construction and experimental validation of a lumped parameter cold-start model (max error 8.9%).
• * Application of a model predictive control (MPC) algorithm to regulate starting current.
• * Comparison of MPC strategy against potentiostatic and maximum power strategies.

Main Results:

• * The MPC cold-start strategy reached the freezing point in 17 seconds at -10 °C.
• * Ice production was effectively controlled to approximately 20 seconds.
• * MPC demonstrated superior performance in optimizing startup time and minimizing potential damage.

Conclusions:

• * The MPC cold-start strategy offers significant advantages over traditional methods.
• * Optimizing startup time while ensuring stack integrity is paramount.
• * Further research can explore additional optimizations for the MPC approach.