Electric buses offer a range of benefits, including a drastic reduction of greenhouse gas emissions compared to personal transit, or to conventional diesel buses. Unfortunately, electric buses also require additional planning to ensure affordable and reliable operation. This thesis proposes two contributions that help to model and plan electric bus deployments, and generally examines how system-focused thinking is required for this application. First, a novel data driven method for estimating the energy consumption of a bus is presented and validated against 1 Hz driving data. Rather than requiring ad hoc data collection, or entire theoretical drivecycle patterns, this new method leverages existing low fidelity driving data from public transit feeds. This data driven method can be used to quickly and accurately model the driving patterns and energy consumption of a whole fleet of buses, as is demonstrated for a case study in Victoria, BC, Canada. Second, using the energy estimating methods previously mentioned, the electricity demand profile for a high-power electric bus charging hub is modelled for various locations and charging systems. Using this modelled demand profile, the potential for using a stationary energy storage system to reduce the peak power demand is investigated. The advantages of three different energy storage technologies (lithium ion, redox flow, and flywheel energy storage systems) are explored. Energy storage was found to be optimal for most charging scenarios modelled, with lithium ion providing the most economical solution for 65% of cases considered. Both the data drive energy estimation modelling, and the energy storage feasibility study constitute novel contributions to the literature. These contributions help to advance the knowledge surrounding electric bus planning and modelling, and help to underpin the systems level thinking required for electric bus deployments. / Graduate