  We propose a gradient-based general computational framework for optimizing
model-dependent parameters in quantum batteries (QB). We apply this method to
two different charging scenarios in the micromaser QB and we discover a
charging protocol for stabilizing the battery in upper-laying Hilbert space
chambers in a controlled and automatic way. This protocol is found to be stable
and robust, and it leads to an improved charging efficiency in micromaser QBs.
Moreover, our optimization framework is highly versatile and efficient, holding
great promise for the advancement of QB technologies at all scales.
