The betavoltaic battery is a type of micro nuclear battery that harvests beta emitting radioactive decay energy using semiconductors. The literature results suggest that a better model is needed to design a betavoltaic battery. This dissertation creates a comprehensive model that includes all of the important factors that impact betavoltaic battery output and efficiency. Recent advancements in micro electro mechanical systems (MEMS) necessitate an onboard miniaturized power source. As these devices are highly functional, longevity of the power source is also preferred. Betavoltaic batteries are a very promising power source that can fulfill these requirements. They can be miniaturized to the size of a human hair. On the other hand, miniaturization of chemical batteries is restricted by low energy density. That is why betavoltaics are a viable option as a power source for sophisticated MEMS devices. They can also be used for implantable medical devices such as pacemakers; for remote applications such as spacecraft, undersea exploration, polar regions, mountains; military equipment; for sensor networks for environmental monitoring; and for sensors embedded in bridges due to their high energy density and long lifetime (up to 100 years). A betavoltaic battery simulation model was developed using Monte Carlo particle transport codes such as MCNP and PENELOPE whereas many researchers used simple empirical equations. These particle transport codes consider the comprehensive physics theory for electron transport in materials. They are used to estimate the energy deposition and the penetration depth of beta particles in the semiconductors. A full energy spectrum was used in the model to take into account the actual radioactive decay energy of the beta particles. These results were compared to the traditional betavoltaic battery design method of estimating energy deposition and penetration depth using monoenergetic beta average energy. Significant differences in results were observed that have a major impact on betavoltaic battery design. Furthermore, the angular distribution of the beta particles was incorporated in the model in order to take into account the effect of isotropic emission of beta decay. The backscattering of beta particles and loss of energy with angular dependence were analyzed. Then, the drift-diffusion semiconductor model was applied in order to estimate the power outputs for the battery, whereas many researchers used the simple collection probability model neglecting many design parameters. The results showed that an optimum junction depth can maximize the power output. The short circuit current and open circuit voltage of the battery varied with the semiconductor junction depth, angular distribution, and different activities. However, the analysis showed that the analytical results overpredicted the experimental results when self-absorption was not considered. Therefore, the percentage of self-absorption and the source thickness were estimated using a radioisotope source model. It was then validated with the thickness calculated from the specific activity of the radioisotope. As a result, the battery model was improved significantly. Furthermore, different tritiated metal sources were analyzed and the beta fluxes were compared. The optimum source thicknesses were designed to increase the source efficiencies. Both narrow and wide band gap semiconductors for beryllium tritide were analyzed. / PHD