|
考虑里程焦虑和用户异质性的电池电动汽车交通网络设计模型
|
Abstract:
纯电动汽车(BEV)清洁、节能的特点是实现交通行业零排放最终目标的更好选择。然而,电动汽车续航里程短、充电时间长和充电设施不足严重阻碍了BEV的普及。随着电动汽车产业的不断发展,BEV的交通网络最优规划成为一个极其重要的话题。本文立足于经典的网络均衡(UE)理论,考虑BEV充电时间,驾驶员里程焦虑和用户异质性,建立了交通网络设计模型,并在既定的投资上限下优化车道扩展方案(即网络中扩展车道的数量和位置)和充电站选址方案。为了求解模型,我们设计了一种局部最优算法并在其中嵌入了列生成避免路径枚举。通过算例分析证明了模型的有效性。最后对不同的政府投资规模进行敏感性分析。结果表明,该模型的建立对城市电动汽车网络设计具有借鉴意义,为驾驶者出行路线的选择和政府交通规划管理提供了参考。
The clean and energy-saving features of pure electric vehicles (BEV) are a better choice to achieve the ultimate goal of zero emissions in the transportation industry. However, short electric vehicle ranges, long charging times, and insufficient charging facilities have severely hindered the widespread adoption of BEVs. With the continuous development of the electric vehicle industry, the optimal planning of BEV transportation network has become an extremely important topic. Based on the classic UE theory, considering BEV charging time, driver mileage anxiety and user heterogeneity, this paper establishes a traffic network design model, and optimizes the lane extension scheme (that is, the number and location of the extended lanes in the network) under the established investment upper limit and charging station construction scheme. To solve the model, we design a local optimum solution algorithm and embed column generation avoidance path enumeration in it. The validity of the model is proved by the example analysis. Finally, sensitivity analysis is carried out on different scales of government investment. The results show that the establishment of this model has reference significance for the design of urban electric vehicle network, and provides a reference for the driver’s travel route selection and government traffic planning management.
[1] | Gowthamraj, R., Aravind, V.C., Norhisam, M., et al. (2021) A Comprehensive Review on System Architecture and International Standards for Electric Vehicle Charging Stations. Journal of Energy Storage, 42, Article ID: 103099.
https://doi.org/10.1016/j.est.2021.103099 |
[2] | Austmann, L.M. and Vigne, S.A. (2021) Does Environmental Awareness Fuel the Electric Vehicle Market? A Twitter Keyword Analysis. Energy Economics, 101, Article ID: 105337. |
[3] | Tang, X., Lin, X. and He, F. (2019) Robust Scheduling Strategies of Electric Buses under Stochastic Traffic Conditions. Transportation Research Part C, 105, 163-182. https://doi.org/10.1016/j.trc.2019.05.032 |
[4] | Song, X.Y. and Wang, Y.L. (2021) Prediction of the Number of Pure Electric Vehicles Based on the Extended GM(1,1) Model. Journal of Physics: Conference Series, 1885, Article ID: 042029.
https://doi.org/10.1088/1742-6596/1885/4/042029 |
[5] | Feng, W. and Figliozzi, M. (2013) An Economic and Technological Analysis of the Key Factors Affecting the Competitiveness of Electric Commercial Vehicles: A Case Study from the USA Market. Transportation Research Part C: Emerging Technologies, 26, 135-145. https://doi.org/10.1016/j.trc.2012.06.007 |
[6] | Chen, Z., Liu, W. and Yin, Y. (2017) Deployment of Stationary and Dynamic Charging Infrastructure for Electric Vehicles along Traffic Corridors. Transportation Research Part C, 77, 185-206. https://doi.org/10.1016/j.trc.2017.01.021 |
[7] | Beckmann, M., Mcguire, C.B. and Winsten, C.B. (1956) Studies in the Economics of Transportation. |
[8] | Minoux, M. (1989) Networks Synthesis and Optimum Network Design Problems: Models, Solution Methods and Applications. Networks, 19, 313-360. https://doi.org/10.1002/net.3230190305 |
[9] | Bell, M.G.H., Pan, J.-J., Teye, C., et al. (2020) An Entropy Maximizing Approach to the Ferry Network Design Problem. Transportation Research Part B, 132, 15-28. https://doi.org/10.1016/j.trb.2019.02.006 |
[10] | Scherr, Y.O., Hewitt, M., Saavedra, B.A.N., et al. (2020) Dynamic Discretization Discovery for the Service Network Design Problem with Mixed Autonomous Fleets. Transportation Research Part B, 141, 164-195.
https://doi.org/10.1016/j.trb.2020.09.009 |
[11] | Wang, X. and Meng, Q. (2017) Discrete Intermodal Freight Transportation Network Design with Route Choice Behavior of Intermodal Operators. Transportation Research Part B, 95, 76-104. https://doi.org/10.1016/j.trb.2016.11.001 |
[12] | Lee, Y.-G., Kim, H.-S., Kho, S.-Y., et al. (2014) User Equilibrium-Based Location Model of Rapid Charging Stations for Electric Vehicles with Batteries That Have Different States of Charge. Transportation Research Record: Journal of the Transportation Research Board, 2454, 97-106. https://doi.org/10.3141/2454-13 |
[13] | Qian, Z. and Rajagopal, R. (2014) Optimal Occupancy-Driven Parking Pricing under Demand Uncertainties and Traveler Heterogeneity: A Stochastic Control Approach. Transportation Research Part B, 67, 144-165.
https://doi.org/10.1016/j.trb.2014.03.002 |
[14] | Liu, Z. and Song, Z. (2018) Network User Equilibrium of Battery Electric Vehicles Considering Flow-Dependent Electricity Consumption. Transportation Research Part C, 95, 516-544. https://doi.org/10.1016/j.trc.2018.07.009 |
[15] | Chen, Z., Liu, W. and Yin, Y. (2017) Deployment of Stationary and Dynamic Charging Infrastructure for Electric Vehicles along Traffic Corridors. Transportation Research Part C: Emerging Technologies, 77, 185-206.
https://doi.org/10.1016/j.trc.2017.01.021 |
[16] | He, F., Yin, Y. and Zhou, J. (2015) Deploying Public Charging Stations for Electric Vehicles on urban road Networks. Transportation Research Part C: Emerging Technologies, 60, 227-240. https://doi.org/10.1016/j.trc.2015.08.018 |
[17] | Wang, S., Meng, Q. and Yang, H. (2013) Global Optimization Methods for the Discrete Network Design Problem. Transportation Research Part B, 50, 42-60. https://doi.org/10.1016/j.trb.2013.01.006 |
[18] | He, F., Yin, Y. and Lawphongpanich, S. (2014) Network Equilibrium Models with Battery Electric Vehicles. Transportation Research Part B, 67, 306-319. https://doi.org/10.1016/j.trb.2014.05.010 |
[19] | Chen, Z., He, F. and Yin, Y. (2016) Optimal Deployment of Charging Lanes for Electric Vehicles in Transportation Networks. Transportation Research Part B: Methodological, 91, 344-365. https://doi.org/10.1016/j.trb.2016.05.018 |
[20] | Schneider, M., Stenger, A. and Goeke, D. (2015) The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations. Transportation Science, 48, 500-520. |
[21] | Goeke, D. and Schneider, M. (2015) Routing a Mixed Fleet of Electric and Conventional Vehicles. European Journal of Operational Research, 245, 81-99. https://doi.org/10.1016/j.ejor.2015.01.049 |
[22] | Wang, Y., Bi, J., Guan, W., et al. (2018) Optimising Route Choices for the Travelling and Charging of Battery Electric Vehicles by Considering Multiple Objectives. Transportation Research Part D: Transport and Environment, 64, 246-261. https://doi.org/10.1016/j.trd.2017.08.022 |
[23] | Pelletier, S., Jabali, O. and Laporte, G. (2019) The Electric Vehicle Routing Problem with Energy Consumption Uncertainty. Transportation Research Part B: Methodological, 126, 225-255. https://doi.org/10.1016/j.trb.2019.06.006 |
[24] | Montoya, A., Guéret, C., Mendoza, J.E., et al. (2017) The Electric Vehicle Routing Problem with Nonlinear Charging Function. Transportation Research Part B: Methodological, 103, 87-110. https://doi.org/10.1016/j.trb.2017.02.004 |
[25] | Aghassi, M., Bertsimas, D. and Perakis, G. (2006) Solving Asymmetric Variational Inequalities via Convex Optimization. Operations Research Letters, 34, 481-490. https://doi.org/10.1016/j.orl.2005.09.006 |
[26] | He, F., Yin, Y. and Lawphongpanich, S. (2014) Network Equilibrium Models with Battery Electric Vehicles. Transportation Research Part B: Methodological, 67, 306-319. https://doi.org/10.1016/j.trb.2014.05.010 |
[27] | Zhang, L., Lawphongpanich, S. and Yin, Y. (2009) An Active-Set Algorithm for Discrete Network Design Problems. In: Transportation and Traffic Theory 2009: Golden Jubilee: Papers Selected for Presentation at ISTTT18, a Peer Reviewed Series since 1959, Springer, Berlin, 283-300. https://doi.org/10.1007/978-1-4419-0820-9_14 |