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Electric vehicle routing problem with machine learning for energy prediction
Transportation Research Part B: Methodological ( IF 6.8 ) Pub Date : 2021-01-22 , DOI: 10.1016/j.trb.2020.12.007
Rafael Basso , Balázs Kulcsár , Ivan Sanchez-Diaz

Routing electric commercial vehicles requires taking into account their limited driving range, which is affected by several uncertain factors such as traffic conditions. This paper presents the time-dependent Electric Vehicle Routing Problem with Chance-Constraints (EVRP-CC) and partial recharging. The routing method is divided into two stages, where the first finds the best paths and the second optimizes the routes. A probabilistic Bayesian machine learning approach is proposed for predicting the expected energy consumption and variance for the road links, paths and routes. Hence it is possible to consider the uncertainty in energy demand by planning charging within a confidence interval. The energy estimation is validated with data from electric buses driving a public transport route in Gothenburg-Sweden as well as with realistic simulations for 24 hours traffic in the city of Luxembourg connected to a high fidelity vehicle model. Routing solutions are compared with a deterministic formulation of the problem similar to the ones found in the literature. The results indicate high accuracy for the energy prediction as well as energy savings and more reliability for the routes.



中文翻译:

机器学习的电动汽车路线规划问题用于能量预测

为电动商用车安排路线时,必须考虑其有限的行驶距离,这会受到交通状况等多种不确定因素的影响。本文提出了带有机会约束(EVRP-CC)和部分充电的时变电动汽车路径问题。路由方法分为两个阶段,第一个阶段找到最佳路径,第二个阶段优化路由。提出了一种概率贝叶斯机器学习方法来预测道路连接,路径和路线的预期能耗和方差。因此,可以通过在置信区间内计划充电来考虑能源需求的不确定性。能源估算通过来自哥德堡-瑞典公共交通路线的电动公交车的数据以及与高保真汽车模型连接的卢森堡市24小时交通的真实模拟进行了验证。将路由解决方案与类似于文献中发现的问题的确定性公式进行比较。结果表明,能量预测的准确性很高,并且节省了能源,路线的可靠性更高。

更新日期:2021-01-22
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