Liu Haoran, Zhang Aobei
School of Science, Shenyang University of Technology, Shenyang, 100870, China.
School of Management, Shenyang University of Technology, Shenyang, 100870, China.
Sci Rep. 2024 Dec 28;14(1):30843. doi: 10.1038/s41598-024-81449-0.
This study presents a novel framework for advancing sustainable urban logistics and distribution systems, with a pivotal focus on fast charging and power exchange modalities as the cornerstone of our research endeavors. Our central contribution encompasses the formulation of an innovative electric vehicle path optimization model, whose paramount objective is to minimize overall operational costs. Integrating V2G technology, we facilitate sophisticated slow charging and discharging management of EVs upon their return to distribution centers, enhancing resource utilization. Moreover, we introduce a robust algorithmic approach for estimating battery degradation costs, meticulously accounting for ambient temperature fluctuations and discharge depth. This methodology, combined with the V2G framework encompassing both charging modes, is effectively solved using a genetic algorithm, ensuring the logistics distribution model's optimal performance. Simulation outcomes underscore the remarkable capacity of our V2G model to augment operational flexibility in EV logistics distribution, culminating in substantial cost reductions. Simultaneously, it adeptly equilibrates peak and off-peak loads within the distribution grid, fostering a more resilient and efficient energy ecosystem. Through rigorous experimental comparisons, we delve into the intricacies of the charging and swapping mode model, offering profound insights that can inform strategic decision-making within the logistics sector regarding optimal charging and swapping strategies. Furthermore, we explore the ramifications of slow charging and discharging management on the distribution system's performance, illuminating their potential benefits. A comprehensive sensitivity analysis is conducted to unravel the factors that influence battery loss in EVs, revealing a pronounced positive correlation between elevated temperatures, deeper discharge depths, and accelerated battery degradation. This revelation underscores the importance of considering environmental conditions in EV operation and maintenance strategies.
本研究提出了一个推进可持续城市物流与配送系统的新颖框架，重点聚焦于快速充电和换电模式，将其作为我们研究工作的基石。我们的核心贡献包括制定一种创新的电动汽车路径优化模型，其首要目标是使总体运营成本最小化。通过整合车辆到电网（V2G）技术，我们在电动汽车返回配送中心时促进了其复杂的慢充和放电管理，提高了资源利用率。此外，我们引入了一种强大的算法方法来估算电池退化成本，精心考虑了环境温度波动和放电深度。该方法与涵盖两种充电模式的V2G框架相结合，通过遗传算法有效求解，确保了物流配送模型的最优性能。仿真结果突出了我们的V2G模型在增强电动汽车物流配送运营灵活性方面的显著能力，最终实现了大幅成本降低。同时，它巧妙地平衡了配电网内的高峰和低谷负荷，培育了一个更具弹性和效率的能源生态系统。通过严格的实验比较，我们深入研究了充电和换电模式模型的复杂性，提供了深刻见解，可为物流行业内关于最优充电和换电策略的战略决策提供参考。此外，我们探讨了慢充和放电管理对配送系统性能的影响，阐明了它们的潜在益处。进行了全面的敏感性分析，以揭示影响电动汽车电池损耗的因素，结果表明温度升高、放电深度加深与电池加速退化之间存在明显的正相关关系。这一发现强调了在电动汽车运营和维护策略中考虑环境条件的重要性。
