Electric bus charging scheduling on hybrid wireless charging network: A multi-factor integrated optimization strategy

As a low-carbon and environment-friendly mode in public transportation, electric buses are favored by many public transport operators due to their lower operating costs. The study focuses on the charging scheduling problem of electric buses in a hybrid wireless charging network, aiming to enhance operational efficiency and reduce costs through optimized charging strategies. This problem is formulated as a mixed integer quadratically constrained programming model, which integrates various factors such as vehicle operation conditions, battery capacity, time-of-use electricity prices, and dynamic adjustment of charging power, with the objective of minimizing the total operating costs of the bus system. To reduce computational complexity, a data preprocessing scheme based on the decomposition and combination of multidimensional arrays is proposed, effectively reducing the time complexity and memory usage of the calculations. In terms of the solution algorithm, the McCormick Envelope linear relaxation method is employed to relax the model, and an adaptive large neighborhood search heuristic algorithm is combined to further enhance computational efficiency. Benchmark instances generated based on real bus route data from Shenzhen City were used to validate the effectiveness of the model through numerical experiments. The results indicate that the optimized charging scheduling strategy can significantly reduce the total operating costs of electric buses: after optimizing the fleet size, the average operating cost per trip decreased by 34.92%. Compared with using a 300 kWh battery, employing a smaller 100 kWh battery reduced the average operating cost per trip by 19.03%. In addition, the study conducted a secondary optimization of charging power and duration, which further optimized the construction of the charging infrastructure. Through multi-factor sensitivity analysis, optimization recommendations were provided for public transport operators. These comprehensive optimizations can reduce energy consumption and operating costs, offering significant theoretical and practical value for the green transformation of urban public transportation systems.