Abstract:

An integrated "battery-vehicle matching" strategy was proposed to address key issues for electric buses in cold regions. These issues include accelerated battery degradation and significant variability in driving range. In this strategy, vehicles are equipped with different capacity batteries based on seasonal temperatures and route demands. This method allows for more optimized vehicle scheduling. A mixed-integer programming model was developed to minimize total operator costs. The model calculated battery acquisition costs, daily maintenance, and charging fees that considered time-of-use rates. A comprehensive function, which quantified both cyclic and calendar battery degradation, was integrated into the cost calculations. This complex model was solved using a specially designed Hybrid Genetic Algorithm (HGA). A study was conducted centered on the actual operating bus routes in Harbin. The results showed significant improvements. The total annualized cost was reduced by 17.8%, and the required fleet size was decreased by 16.4%. Consequently, vehicle acquisition and maintenance costs were saved by 11.5% and 16.8%, respectively. It was also noted that a dual-battery configuration cut annual battery degradation costs by 39.7%, effectively extending the service life of the batteries. Finally, a sensitivity analysis revealed that battery aging can be further slowed by relaxing the maximum state of charge during mild seasons. Additionally, the applicability of the model in larger-scale scenarios is discussed in the concluding section of the instance analysis. This study provides a valuable model and tool for operators to develop seasonal strategies for sustainable operations in cold climates.

Key words: electric bus, vehicle scheduling, genetic algorithm, battery degradation, hybrid battery configuration