During routine operation, the power batteries of electric buses (EBs) gradually age due to the combined effects of numerous factors, including charging/discharging cycles, load fluctuations, and ambient temperature. This paper focuses specifically on the problem of battery aging in the context of actual urban electric bus system operations. It explores how to comprehensively incorporate the battery degradation effect into optimization schemes for EB fleet scheduling. This paper proposes an integrated optimization methodology that combines a capacity degradation model, a scheduling optimization model, and a genetic algorithm. A comprehensive scheduling optimization model is constructed, incorporating vehicle procurement costs, operational charging costs, off-peak charging costs, and battery capacity degradation costs, subject to rigorously defined constraints. Subsequently, an improved genetic algorithm framework is developed. Finally, the constructed model is validated using operational data from the Chongqing bus system. An analysis of the optimization mechanisms is provided, and a sensitivity analysis is conducted on vehicle procurement costs and battery capacity degradation costs. Based on the results, the model can reduce the total cost to 92% of the original level, proving that it is effective to a certain extent in reducing the operating costs of electric buses.
Li et al. (2026) studied this question.