  Energy storage is a fundamental component for the development of sustainable
and environment-aware technologies. One of the critical challenges that needs
to be overcome is preserving the State of Health (SoH) in energy harvesting
systems, where bursty arrival of energy and load may severely degrade the
battery. Tools from Markov process and Dynamic Programming theory are becoming
an increasingly popular choice to control dynamics of these systems due to
their ability to seamlessly incorporate heterogeneous components and support a
wide range of applications. Mapping aging rate measures to fit within the
boundaries of these tools is non-trivial. In this paper, a framework for
modeling and controlling the aging rate of batteries based on Markov process
theory is presented. Numerical results illustrate the tradeoff between battery
degradation and task completion delay enabled by the proposed framework.
