Optimizing fast charger location for hybrid electric bus transit networks

The electrification of buses running on urban transit networks is one of the many weapons in the battle to limit greenhouse gas emissions. Existing diesel buses can be replaced by new fully electric buses or retrofitted to become hybrid. The latter is an interesting alternative in markets where electrification budgets are limited. Hybrid buses can run both on diesel and electric drive modes. They are typically equipped with low-capacity but fast-charging energy storage devices. As a result, their electric range is limited, but they can quickly charge en route while executing their tasks. In this paper, we devise a mixed integer programming model and two versions of a branch-and-check algorithm to locate chargers on multi-line hybrid bus transit networks. More specifically, our methods decide how many chargers to install at each candidate location and what should be the drive mode on each segment of each line in the network. The objective is to maximize the total distance driven using the electric mode. One novelty of our approaches is that they allow for charger sharing between lines. The latter allows for more cost-effective electrification of the network but makes the problem more difficult to solve as line service level and timetabling feasibility constraints become intertwined. We discuss extensive computational experiments on a set of 210 instances based on the transit network of the city of Tours (France). We provide managerial insights into the operational and economic benefits of allowing charger sharing and the trade-offs between increasing the budget and achieving greater electrification. • We introduce a new infrastructure planning problem tailored to hybrid e-bus networks. • We formulate the problem as a mixed integer linear program. • We develop an efficient branch-and-check solution method. • We construct a realistic instance set based on the city of Tours (France). • We show that the methods outperform commercial solvers on large-scale instances.