Fuel Cell Management System: PEMFC Lifetime Optimization by Model Based Approach

Abstract

The estimation and increase of the lifetime of PEMFC fuel cell under dynamic conditions is one of a major challenge about PEMFC. Increasing the durability of the fuel cell must be treated by both the development of new material and design but also by optimal strategies and management of the operating conditions of the fuel cell. Indeed, the different irreversible degradation mechanisms in the MEA (Membrane Electrode Assembly), as the platinum dissolution, Ostwald ripening, carbon support corrosion and chemical membrane degradation, and reversible degradation mechanisms (platinum oxidation, nitrogen stratification, liquid water management) are strongly coupled to the local conditions into the MEA. Moreover, the local conditions into the MEA depend on the material properties but also on the dynamic operating conditions of the fuel cell stack. The strong coupling of the different phenomena can be solved by numerical simulations and an optimum can be found for performance and durability. In this approach, the lifetime of the fuel cell is optimized by a dynamic management of the operating conditions of the fuel cell stack. Indeed, for a same desired power, different set of operating conditions (pressure, temperature, current and stoichiometry) can deliver the same power. Constraints to manage the liquid water inside the fuel cell is also added. A unique cost function, based on a model approach, is built to optimize the fuel cell performance (including compressor consumption and cooling) and the durability of the MEA (irreversible degradations with platinum dissolution and chemical degradation of the membrane and reversible degradation with water management). The different laws used are based on physical models. Their expressions are reduced to be analytical and directly solved. The operating conditions management algorithm is built off-line. It defines the optimum operating conditions of the fuel cell stack (anodic pressure, cathodic pressure, stack temperature, current, air stoichiometry and hydrogen stoichiometry) as a function of the desired power and some fuel cell internal states (water content inside the membrane, water vapor, State of Health SoH). These internal states can be estimated by state observers. The operating conditions management algorithm is validated within a multi-physics fuel cell model. The simulations on a dynamic power cycle show that the degradation rate can be divided by two and compressor consumption also reduced. Moreover, the constraints on the water inside the fuel cell guarantee a correct behavior of the fuel cell stack. The algorithm can also be easily adapted to the fuel cell SoH (State of Health). Figure 1