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Fuel cell (FC) is a viable alternative power source for portable applications; it has higher energy density than traditional Li-ion battery and thus can achieve longer lifetime for the same weight or volume. However, because of its limited power density, it can hardly track fast fluctuations in the load current of digital systems. A hybrid power source, which consists of a FC and a Li-ion battery, has the advantages of long lifetime and good load following capabilities. In this paper, we consider the problem of extending the lifetime of a fuel-cell-based hybrid source that is used to provide power to an embedded system which supports dynamic voltage scaling (DVS). We propose an energy-based optimization framework that considers the characteristics of both the energy consumer (the embedded system) and the energy provider (the hybrid power source). We use this framework to develop algorithms that determine the output power level of the FC and the scaling factor of the DVS processor during task scheduling. Simulations on task traces based on a real-application (Path Finder) and a randomized version demonstrate significant superiority of our algorithms with respect to a conventional DVS algorithm which only considers energy minimization of the embedded system.