Long-haul truck charging planning problem considering time flexibility and energy flexibility

• Published in: Energy (Oxford) Vol. 306; p. 132361
• Main Authors: Wan, Yuchun, He, Zhenggang, Gao, Yufan, Xue, Yujia
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.10.2024
• Subjects: Charging schedule; DBO; Electric battery truck; Long-haul truck; MILP; DBO; Long-haul truck; Electric battery truck; MILP; Charging schedule
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2024.132361

The study explores opportunity charging as a solution for extending electric truck range during routine stops. It proposes a mixed-integer optimization model that combines opportunity charging with truck charging trip planning to optimize charging time and energy. The model examines variability in charging strategy and integrates potential driver choices and behaviors, including hub-charging and en-route charging options. An improved heuristic method is proposed, and the model's effectiveness is verified through numerical experiments. We present the Improved Dung Beetle Optimization (IDBO) algorithm based on the sine-cosine transformation process to solve the model. The proposed IDBO algorithm has the best optimization capability compared to typical genetic algorithms and traditional Dung Beetle Optimization (DBO), and the resulting total cost value was the lowest in all three cases used for the calculation. •Reviews the opportunity charging characteristics of long-haul trucking.•Reduces charging costs by matching long-haul truck charging times and activities to schedule truck charging behavior.•Developing an integrated solution for long-haul truck charging requires considering spatial and temporal aspects, and charging flexibility at different nodes.•This integrated approach aims to optimize the charging solution by strategically placing charging stations and adapting to dynamic scheduling of tariffs and departure times; while adapting to changes in dwell time and energy at different nodes along the route.