基于动态交通推演的电动汽车充电快速引导策略

doi:10.19595/j.cnki.1000-6753.tces.240722

Abstract
Figure/Table
References (39)
Related Citation (15)

Download: PDF (2314 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS) Supporting Info

Abstract

Electric vehicles (EV) have the characteristics of both traffic and mobile load, and their charging behavior will have an interactive impact on the power grid. With the rapid increase in the number of electric vehicle and the continuous improvement of their penetration rate, charging guidance for large-scale EVs has become an important measure to alleviate the contradiction between local limited charging resources and strong charging demand. Therefore, considering the influence of future traffic information changes on navigation strategy, this paper proposes a fast guidance strategy for electric vehicle charging based on dynamic traffic inference.
First of all, a dynamic traffic information prediction model based on spatio-temporal self-supervised learning (ST-SSL) is established. A self-supervised learning (SSL) module for spatial and temporal heterogeneity of traffic data is designed to achieve accurate prediction of multi-period traffic flow information. Secondly, a multi-time dynamic impedance modeling method for urban road network considering future traffic information changes is designed, a charging navigation strategy considering multi-demand scenarios and multi-navigation objectives of users is established, and a solution method based on dynamic Dijkstra algorithm is proposed to realize the selection of the optimal charging station and the planning of the optimal navigation path. Based on the global charging navigation results, the service range of urban charging stations is dynamically evaluated, to achieve rapid charging guidance for electric vehicles. Finally, taking the actual road network of a certain area in Los Angeles as an example, the accuracy of the prediction model and the effectiveness of the guidance strategy are proved, which can effectively perceive the dynamic traffic information and quickly realize the service range division of urban charging stations and the charging guidance for electric vehicles.
In this paper, a fast guidance strategy for electric vehicle charging based on dynamic traffic inference is proposed, based on the case simulation results, the main conclusions can be obtained as follows. ① The model based on ST-SSL can make full use of the spatial and temporal heterogeneity of traffic data, improve the prediction effect of traffic flow information, and provide an effective data basis for the construction of dynamic traffic impedance. ② The proposed multi-scenario and multi-objective charging navigation strategy based on dynamic impedance can effectively perceive traffic information and take into account the diversified needs of users, effectively reduce the cost of charging navigation for different users, and reasonably guide the load distribution of electric vehicles. ③ The proposed dynamic Dijkstra algorithm can recommend the optimal path according to the future traffic information, which can be used as a navigation algorithm to plan the driving path, and can also recommend the customized optimal charging station according to the needs of users. ④ The division of charging station service range based on the global charging navigation results can effectively evaluate the service range of charging station, and provide an important reference for the construction planning of charging station. Based on the evaluation results, the charging navigation strategy is quickly assigned to each node, which effectively reduces the computing resource consumption of charging navigation.

Key words： Electric vehicle traffic system traffic flow prediction charging navigation service coverage

Received: 07 May 2024

PACS:

null

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Hu Junjie
	Pan Yi
	Xu Chengming
	Zhang Kuan
	Wang Fangyu

Cite this article:

Hu Junjie,Pan Yi,Xu Chengming等. Fast Guidance Strategy for Electric Vehicle Charging Based on Dynamic Traffic Inference[J]. Transactions of China Electrotechnical Society, 0, (): 20240722-20240722.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240722 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/20240722

[1] 房宇轩, 胡俊杰, 马文帅. 计及用户意愿的电动汽车聚合商主从博弈优化调度策略[J]. 电工技术学报, 2024, 39(16): 5091-5103.
Fang Yuxuan, Hu Junjie, Ma Wenshuai.Optimal dispatch strategy for electric vehicle aggregators based on Stackelberg game theory considering user intention[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5091-5103.
[2] 兰威, 陈飞雄. 计及阻塞管理的虚拟电厂与配电网协同运行策略[J]. 电气技术, 2022, 23(6): 30-41.
Lan Wei, Chen Feixiong.Cooperative operation strategy of distribution network and virtual power plants considering congestion management[J]. Electrical Engineering, 2022, 23(6): 30-41.
[3] 胡俊杰, 陆家悦, 马文帅, 等. 面向电网调峰的电动汽车聚合商多层级实时控制策略[J/OL]. 电力系统自动化, 1-18[2024-10-23].
Hu Junjie, Lu Jiayue, Ma Wenshuai, et al.Multi-layered real-time control strategy for electric vehicle aggregators aimed at grid peak shaving[J/OL]. Automation of Electric Power Systems, 1-18[2024-10-23].
[4] 范培潇, 杨军, 温裕鑫, 等. 基于可进化模型预测控制的含电动汽车多微电网智能发电控制策略[J]. 电工技术学报, 2024, 39(3): 699-713.
Fan Peixiao, Yang Jun, Wen Yuxin, et al.A multi microgrid intelligent generation control strategy with electric vehicles based on evolutionary model predictive control[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 699-713.
[5] 范培潇, 杨军, 温裕鑫, 等. 考虑电动汽车与微电网参与的配电网双层协调控制策略[J]. 电力系统自动化, 2024, 48(19): 60-68.
Fan Peixiao, Yang Jun, Wen Yuxin, et al.Bi-layer coordinated control strategy of distribution network considering participation of electric vehicles and microgrid[J]. Automation of Electric Power Systems, 2024, 48(19): 60-68.
[6] 吴巨爱, 薛禹胜, 谢东亮, 等. 电动汽车参与电量市场与备用市场的联合风险调度[J]. 电工技术学报, 2023, 38(23): 6407-6418.
Wu Juai, Xue Yusheng, Xie Dongliang, et al.The joint risk dispatch of electric vehicle in day-ahead electricity energy market and reserve market[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6407-6418.
[7] 程瑜, 邰宇峰, 丁肇豪, 等. 基于网络流的共享电动汽车优化调度[J]. 电工技术学报, 2022, 37(增刊1): 145-152.
Cheng Yu, Tai Yufeng, Ding Zhaohao, et al.Optimal scheduling of sharing electric vehicles based on network flow[J]. Transactions of China Electrotechnical Society, 2022, 37(S1): 145-152.
[8] 贾龙, 胡泽春, 宋永华, 等. 储能和电动汽车充电站与配电网的联合规划研究[J]. 中国电机工程学报, 2017, 37(1): 73-84.
Jia Long, Hu Zechun, Song Yonghua, et al.Joint planning of distribution networks with distributed energy storage systems and electric vehicle charging stations[J]. Proceedings of the CSEE, 2017, 37(1): 73-84.
[9] Li Wengen, Cao Jiannong, Guan Jihong, et al.A general framework for unmet demand prediction in on-demand transport services[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(8): 2820-2830.
[10] Tang Liyang, Zhao Yang, Cabrera J, et al.Forecasting short-term passenger flow: an empirical study on Shenzhen metro[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3613-3622.
[11] Diao Zulong, Zhang Dafang, Wang Xin, et al.A hybrid model for short-term traffic volume prediction in massive transportation systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(3): 935-946.
[12] Lin Lu, Li Jianxin, Chen Feng, et al.Road traffic speed prediction: a probabilistic model fusing multi-source data[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(7): 1310-1323.
[13] Duan Peibo, Mao Guoqiang, Liang Weifa, et al.A unified spatio-temporal model for short-term traffic flow prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(9): 3212-3223.
[14] Shin J, Sunwoo M.Vehicle speed prediction using a Markov chain with speed constraints[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(9): 3201-3211.
[15] Guo Shengnan, Lin Youfang, Li Shijie, et al.Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3913-3926.
[16] Liu Jielun, Ong G P, Chen Xiqun.GraphSAGE-based traffic speed forecasting for segment network with sparse data[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(3): 1755-1766.
[17] Qu Zhaowei, Li Haitao, Li Zhihui, et al.Short-term traffic flow forecasting method with M-B-LSTM hybrid network[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(1): 225-235.
[18] Wang Tiange, Zhang Zijun, Tsui K L.PFFN: periodic feature-folding deep neural network for traffic condition forecasting[J]. IEEE Internet of Things Journal, 2024, 11(2): 3108-3120.
[19] Guo Shengnan, Lin Youfang, Feng Ning, et al.Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 922-929.
[20] Zhao Yiji, Lin Youfang, Wen Haomin, et al.Spatial-temporal position-aware graph convolution networks for traffic flow forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(8): 8650-8666.
[21] 刘洪, 张旭, 刘畅, 等. 考虑充电设施充裕性的电动私家车出行与充电需求时序交互分析[J]. 中国电机工程学报, 2018, 38(18): 5469-5478.
Liu Hong, Zhang Xu, Liu Chang, et al.Timing interactive analysis of electric private vehicle traveling and charging demand considering the sufficiency of charging facilities[J]. Proceedings of the CSEE, 2018, 38(18): 5469-5478.
[22] 刘洪, 阎峻, 葛少云, 等. 考虑多车交互影响的电动汽车与快充站动态响应[J]. 中国电机工程学报, 2020, 40(20): 6455-6468.
Liu Hong, Yan Jun, Ge Shaoyun, et al.Dynamic response of electric vehicle and fast charging stations considering multi-vehicle interaction[J]. Proceedings of the CSEE, 2020, 40(20): 6455-6468.
[23] Guo Qinglai, Xin Shujun, Sun Hongbin, et al.Rapid-charging navigation of electric vehicles based on real-time power systems and traffic data[J]. IEEE Transactions on Smart Grid, 2014, 5(4): 1969-1979.
[24] 邢强, 陈中, 冷钊莹, 等. 基于实时交通信息的电动汽车路径规划和充电导航策略[J]. 中国电机工程学报, 2020, 40(2): 534-550.
Xing Qiang, Chen Zhong, Leng Zhaoying, et al.Route planning and charging navigation strategy for electric vehicles based on real-time traffic information[J]. Proceedings of the CSEE, 2020, 40(2): 534-550.
[25] 吴佳龙, 蔡晔, 唐夏菲, 等. 基于Isard法的充电站服务范围划分与实时定价策略[J]. 电力建设, 2023, 44(10): 72-83.
Wu Jialong, Cai Ye, Tang Xiafei, et al.Isard method based charging station service range division and real-time pricing strategy[J]. Electric Power Construction, 2023, 44(10): 72-83.
[26] Ji Jiahao, Wang Jingyuan, Huang Chao, et al.Spatio-temporal self-supervised learning for traffic flow prediction[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(4): 4356-4364.
[27] 沈祖英, 肖雷雷, 单丰武, 等. 环境温度对NCM动力电池系统充放电性能的实验研究[C] // 中国汽车工程学会(China Society of Automotive Engineers). 2020中国汽车工程学会年会论文集, 2020: 8.
Shen Zuying, Xiao Leilei, Shan Fengwu, et al.The experimental study on charge and discharge performance of NCM power battery system based on ambient temperature[C] //SAECCE2020-EV084. 2020: 8.
[28] 中华人民共和国住房和城乡建设部. 城市道路工程设计规范: CJJ 37—2012[S]. 北京: 中国建筑工业出版社, 2012.
[29] 宋媛媛. 基于行驶工况的纯电动汽车能耗建模及续驶里程估算研究[D]. 北京: 北京交通大学, 2014.
Song Yuanyuan.Research on energy consumption modeling and driving range estimation of pure electric vehicles based on driving conditions[D]. Beijing: Beijing Jiaotong University, 2014.
[30] 洛杉矶标准地图数据[EB].(2024-07-06)[2024-07-06]. https://www.openstreetmap.org/export#map=11/33.9898/-118.0488
[31] PeMS Data [EB].(2024-04-20)[2024-04-20]. https://pems.dot.ca.gov/
[32] Kumar S V, Vanajakshi L.Short-term traffic flow prediction using seasonal ARIMA model with limited input data[J]. European Transport Research Review, 2015, 7(3): 21.
[33] Zhang Junbo, Zheng Yu, Qi Dekang.Deep spatio-temporal residual networks for citywide crowd flows prediction[C] //Proceedings of the AAAI Conference on Artificial Intelligence, 2017.
[34] Bai Lei, Yao Lina, Li Can, et al. Adaptive graph convolutional recurrent network for traffic forecasting[EB/OL].2020: 2007.02842.https://arxiv.org/abs/2007.02842v2
[35] Li Mengzhang, Zhu Zhanxing.Spatial-temporal fusion graph neural networks for traffic flow forecasting[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(5): 4189-4196.
[36] 美国电价,12月2023[EB].(2024-03-24)[2024-07-06]. https://zh.globalpetrolprices.com/USA/electricity_prices/
[37] 美国平均时薪(月薪)[EB].(2024-07-06)[2024-07-06]. https://zh.tradingeconomics.com/united-states/average-hourly-earnings
[38] 洛杉矶地区气象数据[EB].(2024-08-24)[2024-08-24]. https://www.ncdc.noaa.gov/cdo-web/datasets/LCD/stations/WBAN:93134/detail
[39] 栾鑫, 邓卫, 程琳, 等. 特大城市居民出行方式选择行为的混合Logit模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1029-1036.
Luan Xin, Deng Wei, Cheng Lin, et al.Mixed Logit model for understanding travel mode choice behavior of megalopolitan residents[J]. Journal of Jilin University (Engineering and Technology Edition), 2018, 48(4): 1029-1036.