Temperature-Optimized Control of PEMFC System Considering Efficiency

Effective thermal management is crucial for proton exchange membrane fuel cells (PEMFCs). However, conventional temperature control strategies predominantly focus on single-objective regulation, proving inadequate for increasingly complex load demands. To maximize PEMFC system efficiency, this study proposes a temperature-optimized adaptive control strategy. The methodology unfolds through four systematic phases: First, semi-empirical mathematical modeling establishes coupled electrochemical-thermal dynamics for both PEMFC stack and thermal management system. Subsequently, a load-current-responsive temperature optimization framework is developed, integrating constrained genetic algorithm (CGA) for global optimal solution identification followed by sequential quadratic programming (SQP) for localized refinement. The optimized temperature setpoints are then implemented via model predictive control (MPC) to achieve precise thermal regulation. Comparative experiments with conventional thermostatic control algorithms demonstrate the proposed strategy's superior efficiency maintenance (3–6% improvement) and dynamic adaptability under variable loading conditions.