Over the last two decades, batteries have become essential components in many high-tech systems, enabling the storage of (electrical) energy for later use. However, the high costs and scarcity of the materials used for high-end batteries make their efficient use and dimensioning a crucial aspect in system design. Enabling such dimensioning requires accurate modeling of the battery behavior under realistic workload conditions. In this paper we use the Kinetic Battery Model (KiBaM) to effectively capture key behavioral aspects of batteries at reasonable modeling costs. Also an accurate battery workload model is needed that describes the demand of energy over time; such workload models often include stochastic elements. Timed automata models have been used to evaluate battery lifetimes under (primarily) deterministic workloads for only small battery configurations. The previously proposed approach required a discretization that led to a computational error that could not be quantified in general. Instead, this paper adopts a stochastic hybrid modeling (SHM) approach to better capture battery dynamics as well as stochastic workloads. This paper, hence, presents an exploration of state-of-the-art SHM methods and tools for the analysis of battery systems under stochastic workloads, and an investigation of the advantages and disadvantages of these techniques.