Enhancing public transportation sustainability: Insights from electric bus scheduling and charge optimization

Abstract

This study presents a joint optimization model for optimizing the scheduling and charging of electric buses in urban transit systems, integrating fleet size determination, trip scheduling, and charging infrastructure planning. The model is solved using a genetic algorithm and validated through constrained particle swarm optimization. Results demonstrate that by efficiently incorporating time-of-use pricing, optimized partial charging, and dynamic speed variations, the model achieves a 2.5% cost reduction compared to full charging and improves operational efficiency by over 7% within changing speed scenarios. Sensitivity analyses confirm the model’s robustness, identifying the minimum charge duration of 15 min and discharge depth of 90% as economically optimal. The study provides valuable insights for transit agencies seeking to optimize electric bus fleet operations and transition to more sustainable and cost-effective public transportation