A generic cost-utility-emission optimization for electric bus transit infrastructure planning and charging scheduling

Implementing battery electric buses (BEB) in transit operation is a promising avenue for reducing greenhouse gas (GHG) emissions. However, challenges are associated with the interdependency of several BEB system parameters during system planning and operation. This study develops a generic optimization model for BEB cost, utility impact, and GHG emissions. The model optimizes the sizing/location of the charging infrastructure, onboard battery capacity, and charging schedule. Furthermore, a trip-level energy consumption model is embedded in the optimization process to accommodate the varying energy consumption rates at the trip level. The optimization model is applied to a mid-size multi-hubs transit network. The results indicate that both en-route and depot charging approaches are required, with varying power capacities (heterogeneous infrastructure) and the number of chargers (poles). Furthermore, the temporal variation of the electricity time-of-use and GHG emissions intensity play significant roles in the resultant charging strategy and, thus, the system cost. Overall, the results indicate that the inclusion of all design parameters as decision variables in the model, as proposed in this study, is essential to account for the intertwined synergy of the BEB system's components.

Automated summary

Implementing battery electric buses (BEB) in transit operation can effectively reduce greenhouse gas emissions, but it faces challenges due to the interdependency of various system parameters. This study develops an optimization model that considers BEB cost, utility impact, and GHG emissions, optimizing the charging infrastructure, battery capacity, and charging schedule. The results show that both en-route and depot charging approaches are necessary, with different power capacities and charger quantities. The temporal variation of electricity time-of-use and GHG emissions intensity also greatly influence the charging strategy and system cost.