Optimization of Energy Replenishment for Inland Electric Ships Considering Multi-Technology Adoption and Partial Replenishment

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Publicat a:Journal of Marine Science and Engineering vol. 13, no. 11 (2025), p. 2092-2118
Autor principal: Guo Siqing
Altres autors: Wang, Yubing, Yue Mingyuan, Dai Lei, Fang Sidun, Zhang Shenxi, Hu, Hao
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MDPI AG
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100 1 |a Guo Siqing  |u State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; gsq0505@sjtu.edu.cn (S.G.); cauchmar@sjtu.edu.cn (Y.W.); yuemingyuan@sjtu.edu.cn (M.Y.); hhu@sjtu.edu.cn (H.H.) 
245 1 |a Optimization of Energy Replenishment for Inland Electric Ships Considering Multi-Technology Adoption and Partial Replenishment 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a While battery-powered propulsion represents a promising pathway for inland waterway freight, its widespread adoption is hindered by range anxiety and high investment costs. Strategic energy replenishment has emerged as a critical and cost-effective solution to extend voyage endurance and mitigate these barriers. This paper introduces a novel approach to optimize energy replenishment strategies for inland electric ships that considers the possibility of adopting multiple technologies (charging and battery swapping) and partial replenishment. The proposed approach not only identifies optimal replenishment ports but also determines the technology to employ and the corresponding amount of energy to replenish for each operation, aimed at minimizing total replenishment costs. This problem is formulated as a mixed-integer linear programming model. A case study of a 700-TEU electric container ship operating on two routes along the Yangtze River validates the effectiveness of the proposed approach. The methodology demonstrates superior performance over existing approaches by significantly reducing replenishment costs and improving solution feasibility, particularly in scenarios with tight schedules and limited technology availability. Furthermore, a sensitivity analysis examines the impacts of key parameters, offering valuable strategic insights for industry stakeholders. 
653 |a Linear programming 
653 |a Integer programming 
653 |a Ports 
653 |a Sensitivity analysis 
653 |a Parameter sensitivity 
653 |a Electric vehicles 
653 |a Optimization 
653 |a Container ships 
653 |a Ships 
653 |a Energy consumption 
653 |a Technology adoption 
653 |a Energy 
653 |a Energy costs 
653 |a Cargo ships 
653 |a Decision making 
653 |a Flexibility 
653 |a Effectiveness 
653 |a Replenishment 
653 |a Energy efficiency 
653 |a Mixed integer 
653 |a Inland waterways 
653 |a Cost control 
653 |a Inland waters 
653 |a Economic 
700 1 |a Wang, Yubing  |u State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; gsq0505@sjtu.edu.cn (S.G.); cauchmar@sjtu.edu.cn (Y.W.); yuemingyuan@sjtu.edu.cn (M.Y.); hhu@sjtu.edu.cn (H.H.) 
700 1 |a Yue Mingyuan  |u State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; gsq0505@sjtu.edu.cn (S.G.); cauchmar@sjtu.edu.cn (Y.W.); yuemingyuan@sjtu.edu.cn (M.Y.); hhu@sjtu.edu.cn (H.H.) 
700 1 |a Dai Lei  |u State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; gsq0505@sjtu.edu.cn (S.G.); cauchmar@sjtu.edu.cn (Y.W.); yuemingyuan@sjtu.edu.cn (M.Y.); hhu@sjtu.edu.cn (H.H.) 
700 1 |a Fang Sidun  |u School of Electrical Engineering, Chongqing University, Chongqing 400044, China; fangston@foxmail.com 
700 1 |a Zhang Shenxi  |u School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; willzsx@sjtu.edu.cn 
700 1 |a Hu, Hao  |u State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; gsq0505@sjtu.edu.cn (S.G.); cauchmar@sjtu.edu.cn (Y.W.); yuemingyuan@sjtu.edu.cn (M.Y.); hhu@sjtu.edu.cn (H.H.) 
773 0 |t Journal of Marine Science and Engineering  |g vol. 13, no. 11 (2025), p. 2092-2118 
786 0 |d ProQuest  |t Engineering Database 
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