Agent-based modeling and simulation for the pricing strategy of the electric vehicle battery switching station

被引：0

作者：

Han, Peng ^{[1
]}

Wang, Jinkuan ^{[1
]}

Han, Yinghua ^{[1
]}

Li, Yan ^{[1
]}

机构：

[1] School of Information Science and Engineering, Northeastern University

来源：

Journal of Computational Information Systems | 2014年 / 10卷 / 07期

关键词：

Agent-based modeling; Battery condition; Battery switching station; Electric vehicle; Pricing strategy;

D O I：

10.12733/jcis9774

中图分类号：

学科分类号：

摘要：

The pricing strategy is critical for the construction and operation of the battery switching stations as well as the service quality to the EV owners. In this paper, operation cost of the battery switching station is firstly analyzed, and then an agent-based model of the battery switching service is proposed, which enables the observation of the EV refueling request, the simulation of the battery switching station, and the assessment of different pricing strategies. Furthermore, considering the current high cost of the EV batteries and their quality decreasing due to recharging times, this paper proposed a batterycondition- based pricing strategy. And developed a 3D simulation platform to verify the effectiveness of the proposed model and the performance of the pricing strategy. Simulation denotes that the model can well reveal the driving mode of the EV owners and the battery conditions, which will be of significant meanings in making decisions about the configuration of the station and the pricing strategy. And due to the fluctuation in the battery quality in the simulation, the cost of the battery is dominant in the cost of the station compared with the cost of the electricity, and the proposed pricing is a preferred way in making up the battery cost of the station while providing a fair service for the EV owners. © 2014 Binary Information Press.

引用

页码：2803 / 2812

页数：9

共 13 条

[1]

Han P., Wang J.K., Han Y.H., Zhao Q., Novel WSN-based residential energy management scheme in smart grid, IEEE International Conference on Information Science and Technology, pp. 393-396, (2012)

[2]

Han P., Wang J.K., Han Y.H., Lei T., Assessment of smart grid PEV charging management Mechanism in grid safety and environmental impact, Advances in Information Sciences and Service Sciences, 4, pp. 144-152, (2012)

[3]

Han P., Wang J.K., Han Y.H., Dynamic-priority-based real-time charging management for plug-in electric vehicles in smart grid, Journal of University of Science and Technology of China, 42, pp. 100-105, (2012)

[4]

Li Y., Wang J.K., Han P., Han Y.H., Modeling and Analysis on coordinated scheduling of ebus recharging station participated wind-power generation, 32nd Chinese Control Conference, pp. 8592-8596, (2012)

[5]

Peterson S., Whitacre J., Apt J., The economics of using PHEV battery packs for grid storage, (2009)

[6]

Rong P., Pedram M., An analytical model for predicting the remaining battery capacity of lithium-ion batteries, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 14, pp. 441-451, (2006)

[7]

Liu N., Tang X., Duan S., Zhang J., Capacity optimization method for PV-based battery swapping Stations considering second-use of electric vehicle batteries, Proceedings of the CSEE, 33, pp. 34-44, (2013)

[8]

National household travel survey, (2009)

[9]

Han P., Wang J.K., Han Y.H., Analysis and modeling for the PEV charging regularity impact to the distribution grid, Journal of Northeastern University (Natural Science), 33, pp. 1702-1705, (2012)

[10]

Zhou Z., Chan W.K., Chow J.H., Agent-based simulation of electricity markets: A survey of tools, Artificial Intelligence Review, 28, pp. 305-342, (2007)

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