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Electric buses are environmentally friendly with the features of low noise levels and zero-emissions. However, higher upfront costs due to the battery degradation effect, charging facility capacity and operational issues are the main obstacles to large-scale application of electric buses. This study proposes a mixed-integer nonlinear model for optimizing electric bus scheduling with a partial charging strategy, battery degradation and constraints of charging facility capacity. A genetic algorithm is then proposed to solve the model. Last, a case study based on a real transit network in Nanjing, China is conducted. The experimental results show that compared with the existing scheduling scheme, the optimal scheduling model can reduce the total system costs by 8.20%, which validates the effectiveness of the proposed model. The findings in this paper can provide a reference for operators to formulate electric bus scheduling schemes and promote the sustainable development of public transport.
Zhang et al. (Sun,) studied this question.