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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.10157 |
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| _version_ | 1866915491823484928 |
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| author | Guo, Jia Irannezhad, Elnaz |
| author_facet | Guo, Jia Irannezhad, Elnaz |
| contents | Battery electric freight trains are crucial for decarbonization by providing zero-emission transportation alternatives. The proper adoption of battery electric freight trains depends on an efficient battery electrification strategy, involving both infrastructure setup and charge scheduling. The study presents a comprehensive model for the optimal design of charging infrastructure and charge scheduling for each train. To provide more refueling flexibility, we allow batteries to be either charged or swapped in a deployed station, and each train can carry multiple batteries. This problem is formulated as a mixed integer linear programming model. To obtain real-time solutions for a large scale network, we develop three algorithms to solve the optimization problem: (1) a Rectangle Piecewise Linear Approximation technique, (2) a Fixed Algorithm heuristic, and (3) Benders Decomposition algorithm. In computational experiments, we use the three proposed algorithms to solve instances with up to 25 stations. Statistical analysis verifies that Benders Decomposition outperforms the other two algorithms with respect to the objective function value, closely followed by the Rectangle Piecewise Linear Approximation technique, and the Fixed Algorithm provides the least optimal solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10157 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Freight Rail Electrification: A Framework for Charge Station Selection and Battery Charge/Swap Scheduling Guo, Jia Irannezhad, Elnaz Computational Engineering, Finance, and Science 90B06 Battery electric freight trains are crucial for decarbonization by providing zero-emission transportation alternatives. The proper adoption of battery electric freight trains depends on an efficient battery electrification strategy, involving both infrastructure setup and charge scheduling. The study presents a comprehensive model for the optimal design of charging infrastructure and charge scheduling for each train. To provide more refueling flexibility, we allow batteries to be either charged or swapped in a deployed station, and each train can carry multiple batteries. This problem is formulated as a mixed integer linear programming model. To obtain real-time solutions for a large scale network, we develop three algorithms to solve the optimization problem: (1) a Rectangle Piecewise Linear Approximation technique, (2) a Fixed Algorithm heuristic, and (3) Benders Decomposition algorithm. In computational experiments, we use the three proposed algorithms to solve instances with up to 25 stations. Statistical analysis verifies that Benders Decomposition outperforms the other two algorithms with respect to the objective function value, closely followed by the Rectangle Piecewise Linear Approximation technique, and the Fixed Algorithm provides the least optimal solution. |
| title | Optimizing Freight Rail Electrification: A Framework for Charge Station Selection and Battery Charge/Swap Scheduling |
| topic | Computational Engineering, Finance, and Science 90B06 |
| url | https://arxiv.org/abs/2509.10157 |