Fuel cell systems (FCSs) are based on a number of components whose electrochemical and physical interactions during operation are not fully understood either by empirical experience or by extensive modeling. The related FCS complexity limits technological applications and success. To exploit the potential of FCS-based deployments, this paper presents a complete and consistent toolchain that enables their model-based understanding and multi-objective optimization (MOO) regarding design and operation. The added value of the generic process is demonstrated by a MOO of an automotive FCS power train with fuel consumption and fuel cell aging as conflicting targets. For this purpose, an open-source object-oriented FCS model was extended to include cell degradation effects at stack level and power train components at system level. The resulting model covers the essential interactions at system level and enables the MOO of both the parametric design of the system components and their operational management. Highly efficient algorithms are used for MOO in view of the model’s considerable computing effort. In the context of the selected target criteria, this enables a comprehensive analysis of the technological potential in the form of target variable conflicts as well as the sensitivities of these target variables in their dependence on design and operational degrees of freedom. The results, which can be transferred to other conflicting target variables and their simultaneous optimization, demonstrate the fundamental superiority of a Pareto-optimal system design and operational management over previous development methods.

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