工业工程2024，Vol.27Issue(4)：132-140,149,10.DOI:10.3969/j.issn.1007-7375.230095
考虑未来运营收益的自动驾驶出租车充放电协同路径规划
Charging and Discharging Coordinated Routing for Autonomous Electric Taxis Considering Future Operating Value
摘要
Abstract
Existing taxi scheduling models typically focus on the optimization of real-time cost while the potential impact of currently planned routes on future operating value is ignored,which is detrimental to continuous scheduling in autonomous driving environment.To this end,this paper proposes a route planning model focusing on long-term benefit,in which the estimated future operating value is incorporated into the real-time scheduling problem by reinforcement learning.Specifically,the model is solved by a neural network first to fit the state-value function for different temporal and spatial states of vehicles,after which a double neural network and the experience replay are used to accelerate the convergence of the algorithm.Through the simulation experiments on the road network of Shenzhen,it demonstrates that our model enables to accurately schedule the fleet in advance,serving more passengers and achieving greater operational profit.Additionally,the cost of fleet energy consumption can be reduced since the model can utilized the peak and off-peak characteristics of time-of-use electricity pricing and vehicle-to-grid(V2G)technology for charging and discharging.Compared to other scheduling models,the proposed model enables to increase the passenger response rate by 4%and the total profit by 25%in long-term operation,which also reduces energy consumption by 50%and passenger waiting time by 20%.关键词
未来运营收益/强化学习/分时电价/电动汽车入网技术/状态价值函数Key words
future operating value/reinforcement learning/time-of-use electricity pricing/vehicle-to-grid technology/state-value function分类
信息技术与安全科学引用本文复制引用
曾伟良,韩宇,傅惠..考虑未来运营收益的自动驾驶出租车充放电协同路径规划[J].工业工程,2024,27(4):132-140,149,10.基金项目
国家自然科学基金资助项目(62273102) （62273102）
广东省基础与应用基础研究基金资助项目(2024A1515010629) （2024A1515010629）
