Dynamic voltage scaling (DVS), which adjusts the clock speed and supply voltage dynamically, is an effective technique in reducing the energy consumption of embedded real-time systems. The energy efficiency of a DVS algorithm largely depends on the performance of the slack estimation method used in it. In this paper, we propose a novel DVS algorithm for periodic hard real-time tasks based on an improved slack estimation algorithm. Unlike the existing techniques, the proposed method takes full advantage of the periodic characteristics of the real-time tasks under priority-driven scheduling such as EDF. Experimental results show that the proposed algorithm reduces the energy consumption by 20˜40% over the existing DVS algorithm. The experiment results also show that our algorithm based on the improved slack estimation method gives comparable energy savings to the DVS algorithm based on the theoretically optimal (but impractical) slack estimation method.