Advanced Self-Adapted Predictive Control Strategy for High-Power Vehicular Fuel Cell System Thermal Management

Effective thermal management is essential for the performance of high-power fuel cell systems, especially for vehicular applications. This study presents a novel self-adaptive predictive control (SAPC) strategy to address challenges posed by large time delay effects and time-variant characteristics in fuel cell systems. By integrating prediction and current information, SAPC reduces the time-domain signal mismatch induced by delay effects in the basic proportional-integral-derivative (PID) controller. Adaptive weight factors for the prediction component are tuned online for various scenarios, and online/offline model adjustments automatically adapt the dynamic transfer-function model to varying operational points and time-variant characteristics. Experimental validation on an 80 kW fuel cell system test bench demonstrates that SAPC significantly reduces temperature control error by 59% and 64.5%, overshoot by 74.6% and 70%, and mean settling time by 87.9% and 77.6% compared with two other conventional PID frameworks. This highlights SAPC as a promising solution for enhancing precise temperature control in high-power fuel cell systems within dynamic load conditions.