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Battery electric buses (BEBs) are increasingly used in urban transit due to their environmental and economic benefits. However, the conventional “charge-at-stop” approach often neglects time-of-use (TOU) electricity pricing, charger limits, and battery degradation, leading to cost inefficiencies. This study proposes a simulation–optimization framework that integrates SUMO-based traffic modeling and Mixed-Integer Programming (MIP)-based charging optimization. Real-time monitoring of the state of charge (SOC) and energy data is extracted using Python–TraCI. An MIP model is then developed to minimize total charging costs under TOU pricing and segmented power constraints. Simulation on Hohhot’s bus network shows a 14.56% cost reduction and 92.16% less charging during peak-price periods, confirming the effectiveness of the proposed strategy.

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Integrated Charging Cost Optimization for Electric Bus Fleets Based on Traffic Simulation and Mixed-Integer Programming

  • 会议论文

  • 发表日期：

    2026年

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  • Yanan Hu,
  • Yuan Zhu,
  • Shaojia Yuan,
  • Yuchun Huang

摘要

Battery electric buses (BEBs) are increasingly used in urban transit due to their environmental and economic benefits. However, the conventional “charge-at-stop” approach often neglects time-of-use (TOU) electricity pricing, charger limits, and battery degradation, leading to cost inefficiencies. This study proposes a simulation–optimization framework that integrates SUMO-based traffic modeling and Mixed-Integer Programming (MIP)-based charging optimization. Real-time monitoring of the state of charge (SOC) and energy data is extracted using Python–TraCI. An MIP model is then developed to minimize total charging costs under TOU pricing and segmented power constraints. Simulation on Hohhot’s bus network shows a 14.56% cost reduction and 92.16% less charging during peak-price periods, confirming the effectiveness of the proposed strategy.