To address the challenges such as accelerated battery lifespan degradation and significant fluctuations in driving range caused by extreme temperature variations in cold-region electric buses, this study proposes an integrated scheduling strategy based on " battery-vehicle matching". This strategy involves equipping buses with batteries of different capacities according to seasonal temperatures and route load demands, enabling public transport operators to formulate more efficient operational plans. In terms of modeling, a mixed-integer programming model is established with the objective of minimizing operator cost. The model comprehensively calculates vehicle battery procurement expenses, daily maintenance costs, and charging expenses that consider time-of-use electricity pricing and demand charges. It quantifies both cyclic and calendar degradation of batteries and integrates them into a comprehensive battery lifespan degradation function incorporated into the overall cost calculation. For algorithm design, a hybrid genetic algorithm (HGA) incorporating a post-processing refinement strategy is developed to efficiently solve this NP-hard model. A case study based on actual bus routes in Harbin demonstrates that the optimized strategy reduces the annualized total cost by 17.8%, decreases the required fleet size by 16.4%, and lowers maintenance costs by 16.8%. It is obvious that the dual battery configuration reduces the annualized degradation cost of battery ownership cost by 39.7 %, effectively extending the actual service life of battery assets.Sensitivity analysis shows that appropriately relaxing the state-of-charge (SOC) upper limit during moderate-temperature seasons (e. g., spring and autumn) can reduce cycling frequency, thereby better mitigating battery aging. Finally, the applicability of the model to larger-scale scenarios is discussed. This study provides a theoretical model and optimization tool for the sustainable operation of electric buses in cold regions, and plays an important role in formulating seasonal operation strategies for operators.

To address the issue of unbalanced task distribution between electric bus vehicles and drivers in loop line, this study proposed a joint optimal scheduling model, which mainly improves the overall utilization rate by adjusting vehicles and drivers in clockwise and counterclockwise directions. Given a fixed loop route and non-fixed vehicle-driver assignments, the model considers various constraints such as vehicle mileage, workload, number of charging stations, charging duration, driver working and rest times. It aims to minimize both the total operating cost of the transit enterprise and the total timetable adjustment, while formulating an orderly charging management plan and scheduling strategy for vehicles and drivers. In the aspect of solution, the mixed integer nonlinear programming model was transformed into linear programming model by linear transformation, and the scheduling scheme was obtained by using CPLEX solver. Additionally, a multi-objective particle swarm algorithm (MOPSO) and improved multi-objective particle swarm algorithm (ε-MOPSO) based on constraint processing mechanism were used to solve the scheduling scheme respectively, and the convergence and uniformity of external file set were ensured by grid method. The proposed approach is validated through a case study on Beijing’s Route 200 (inner and outer loop lines). A comparative analysis of the results obtained from the CPLEX solver, the traditional MOPSO, and the improved ε-MOPSO confirms the effectiveness of the improved algorithm. The optimized scheduling plan reduces the number of vehicles from 28 to 23 (a 17.86% reduction) and the number of drivers from 28 to 25 (a 10.71% reduction), thereby lowering the total operating cost. The timetable adjustments average 4.13 minutes per departure, resulting in more evenly spaced departures and better meeting passenger demand. This significantly enhances the operational efficiency of public transportation and holds substantial practical significance.