Dynamic thermal management of proton exchange membrane fuel cell vehicle system using the tube-based model predictive control

The thermal management of proton exchange membrane fuel cell crucially impacts the performance and durability of fuel cell vehicle systems. Optimized temperature control is critical to the water balance and electrochemical reaction inside the fuel cell stack under dynamic operating conditions. This work develops a dynamic thermal management system model with uncertain parameters for the dual coolant circulation fuel cell system to simulate unforeseeable disturbances in real systems. With the proposed model, the tube-based robust model predictive control (tube-RMPC) is innovatively applied to optimize dynamic thermal management. This strategy can identify the optimal trajectories of thermostat angle, heater power, water pump speed, and cooling fan speed, thus maximizing the temperature dynamic response speed while minimizing the stack temperature difference fluctuations in the presence of unforeseeable disturbances. The result shows that during the load ascending and descending operations, the temperature dynamic response time is 8 and 15 s, with temperature fluctuations below 1 degrees C. The root mean square error for stack temperature is reduced by 10.4% and 24.4% compared to the closed-loop MPC. The fuel cell system exhibits excellent temperature dynamic response capability using the tube-RMPC method.