Joint Optimization of Battery Sizing and Charging Infrastructure for Urban Electric Bus Networks: A Case Study of Ulm

The electrification of urban public transport represents a critical pathway for reducing greenhouse gas emissions and improving air quality in metropolitan areas. This thesis develops and applies a comprehensive optimization framework for the strategic planning of charging infrastructure and battery sizing for electric bus networks, using the city of Ulm, Germany as a case study. A mixed-integer linear programming (MILP) model was developed to simultaneously optimize charging infrastructure placement, battery capacity selection, and charging scheduling strategies while minimizing the net present value of total cost of ownership over a 15-year planning horizon. The model integrates statistical energy consumption based on publicly available GTFS transit data with economic optimization, accounting for battery cost projections, time varying electricity prices, and operational constraints. Multiple scenarios were analyzed, including different battery cost decline rates, charger availability restrictions, and electricity pricing structures. Results demonstrate that a mixed charging strategy combining depot charging with opportunity charging achieves the lowest total system cost of 79.23 M€. Bus acquisition costs dominate the economic structure at 59.35 M€, followed by charging infrastructure at 13.1 M€ and batteries at 6.78 M€. Time-varying electricity pricing consistently outperformed fixed tariffs, achieving cost reductions of 1.9-7.6% depending on temperature conditions. Sensitivity analysis identified temperature as the most critical design parameter, with battery cost projections showing non-linear effects on optimal configurations. Eliminating low-cost 50 kW depot chargers resulted in cost penalties of 12.3-12.4%, while standardizing opportunity charging equipment increased costs by only 1.9-2.0%.