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 op...
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| Vydáno v: | Energy (Oxford) Ročník 329; s. 136499 |
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| Jazyk: | angličtina |
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Elsevier Ltd
15.08.2025
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| ISSN: | 0360-5442 |
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| Abstract | 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.
•MIQCP model optimizes electric bus charging in hybrid wireless networks.•Data preprocessing and McCormick Envelope reduce computational complexity.•ALNS algorithm enhances efficiency for large-scale scheduling problems.•Fleet and battery optimization cut trip costs by 34.92% and 19.03%.•Multi-factor analysis guides infrastructure planning and cost reduction. |
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| AbstractList | 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.
•MIQCP model optimizes electric bus charging in hybrid wireless networks.•Data preprocessing and McCormick Envelope reduce computational complexity.•ALNS algorithm enhances efficiency for large-scale scheduling problems.•Fleet and battery optimization cut trip costs by 34.92% and 19.03%.•Multi-factor analysis guides infrastructure planning and cost reduction. |
| ArticleNumber | 136499 |
| Author | Tang, Jinjun Chen, Taolue Sun, Chao Li, Mingyang Liang, Xiao |
| Author_xml | – sequence: 1 givenname: Taolue orcidid: 0009-0008-7306-8539 surname: Chen fullname: Chen, Taolue organization: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China – sequence: 2 givenname: Chao surname: Sun fullname: Sun, Chao organization: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China – sequence: 3 givenname: Xiao surname: Liang fullname: Liang, Xiao email: 2742@hutb.edu.cn organization: School of Foreign Studies, Hunan University of Technology and Business, Changsha, 410205, China – sequence: 4 givenname: Mingyang surname: Li fullname: Li, Mingyang organization: Smart Transport Key Laboratory of Hunan Province, School of Transport and Transportation Engineering, Central South University, Changsha, 410075, China – sequence: 5 givenname: Jinjun surname: Tang fullname: Tang, Jinjun organization: Smart Transport Key Laboratory of Hunan Province, School of Transport and Transportation Engineering, Central South University, Changsha, 410075, China |
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| Keywords | Wireless charging Adaptive large neighborhood search Sensitivity analysis Electric bus charging scheduling Mixed integer quadratically constrained programming |
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| Snippet | As a low-carbon and environment-friendly mode in public transportation, electric buses are favored by many public transport operators due to their lower... |
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| SubjectTerms | Adaptive large neighborhood search Electric bus charging scheduling Mixed integer quadratically constrained programming Sensitivity analysis Wireless charging |
| Title | Electric bus charging scheduling on hybrid wireless charging network: A multi-factor integrated optimization strategy |
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