Electric vehicle charging situation awareness for charging station
ultra-short-term load forecast

doi:10.1007/s12204-023-2566-2

Abstract

Abstract: Electric vehicles (EVs) are expected to be key nodes connecting transportation–electricity–communication networks. Advanced automotive electronics technologies enhance EVs’ perception, computing, and communication capacity, which in turn can boost the operational efficiency of intelligent transportation systems (ITSs). EVs couple the ITS to the power system, providing a promising solution to charging congestion and transformer overload via navigation and forecasting approaches. This study proposes a privacy-preserving EV charging situation awareness framework and method to forecast the ultra-short-term load of charging stations. The proposed method only relies on public information from commercial service providers. In the case study, data are powered by the Baidu LBS cloud and EV-SGCC platform, and the experiment is conducted within an area of Pudong New District in Shanghai. Based on the results, the charging load of charging stations can be adequately forecasted more than 1 min ahead with low communication and computing power requirements. This research provides the basis for further studies on operation optimization and electricity market transaction of charging stations.

Key words: electric vehicle (EV), intelligent transportation system (ITS), situation awareness, charging load forecast

摘要： 电动汽车将成为连接智能交通系统与电力系统的关键节点，先进的汽车电子技术提高了电动车的感知、计算和通信能力。这些技术为充电导航和充电负荷预测提供了条件，为充电拥堵和变压器过载问题提供了一种解决方案。本研究提出了一种保护车主隐私的充电态势感知框架和方法，仅依赖于出行服务商提供的公开信息，可开展充电站侧的超短期负荷预测。案例分析中，选取上海市浦东新区的一个区域开展实验，从在线地图平台和充电服务平台采集数据。结果显示，基于态势感知的方法，在低通信和算力要求下，各站的充电负荷可以提前1分钟以上得到准确预测。本工作为进一步研究充电站优化运行策略和电力市场环境中的交易模式提供了基础。

关键词: 电动汽车，智能交通系统，态势感知，充电负荷预测

CLC Number:

TM73
U469.72

SHI Yiwei1 (史一炜), LIU Zeyu1 (刘泽宇), FENG Donghan1∗ (冯冬涵), ZHOU Yun1∗ (周云), ZHANG Kaiyu2 (张开宇), LI Hengjie3 (李恒杰). Electric vehicle charging situation awareness for charging station ultra-short-term load forecast[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 28-38.

References

[1] TIE S F, TAN C W. A review of energy sources and energy management system in electric vehicles [J]. Renewable and Sustainable Energy Reviews, 2013, 20: 82-102.
[2] EHSANI M, SINGH K V, BANSAL H O, et al. State of the art and trends in electric and hybrid electric vehicles [J]. Proceedings of the IEEE, 2021, 109(6): 967-984.
[3] JING X, YAO X F. Towards social cyber-physical production systems [J]. Acta Automatica Sinica, 2019, 45(4): 637-656 (in Chinese).
[4] HAN S S, WANG X, ZHANG J J, et al. Parallel vehicular networks: A CPSS-based approach via multimodal big data in IoV [J]. IEEE Internet of Things Journal, 2019, 6(1): 1079-1089.
[5] MAZUMDER S K, KULKARNI A, SAHOO S, et al. A review of current research trends in power-electronic innovations in cyber-physical systems [J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021, 9(5): 5146-5163.
[6] WANG L, NIAN V, LI H L, et al. Impacts of electric vehicle deployment on the electricity sector in a highly urbanised environment [J]. Journal of Cleaner Production, 2021, 295: 126386.
[7] MURATORI M. Impact of uncoordinated plug-in electric vehicle charging on residential power demand [J]. Nature Energy, 2018, 3(3): 193-201.
[8] LI G Y, SUN Q, BOUKHATEM L, et al. Intelligent vehicle-to-vehicle charging navigation for mobile electric vehicles via VANET-based communication [J]. IEEE Access, 2019, 7: 170888-170906.
[9] ATALLAH R, KHABBAZ M, ASSI C. Multihop V2I communications: A feasibility study, modeling, and performance analysis [J]. IEEE Transactions on Vehicular Technology, 2017, 66(3): 2801-2810.
[10] HANNAN M A, HOQUE M M, HUSSAIN A, et al. State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applications: Issues and recommendations [J]. IEEE Access, 2018, 6: 19362-19378.
[11] ZHUANG H Y, QIAN Y Q, YANG M. Intelligent connected vehicle as the new carrier towards the era of connected world [J]. Journal of Shanghai Jiao Tong University (Science), 2021, 26(5): 559-560.
[12] RIGAS E S, RAMCHURN S D, BASSILIADES N. Managing electric vehicles in the smart grid using artificial intelligence: A survey [J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 1619-1635.
[13] MOGHADDAM Z, AHMAD I, HABIBI D, et al. Smart charging strategy for electric vehicle charging stations [J]. IEEE Transactions on Transportation Electrification, 2018, 4(1): 76-88.
[14] SCHNEIDER M, STENGER A, GOEKE D. The electric vehicle-routing problem with time windows and recharging stations [J]. Transportation Science, 2014, 48(4): 500-520.
[15] XIN S J, GUO Q L, SUN H B, et al. A hybrid simulation method for EVs’ operation considering power grid and traffic information [C]//IEEE Power & Energy Society General Meeting. Vancouver: IEEE, 2013: 1-5.
[16] GUO Q L, XIN S J, SUN H B, 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. [17] YANG H M, DENG Y J, QIU J, et al. Electric vehicle route selection and charging navigation strategy based on crowd sensing [J]. IEEE Transactions on Industrial Informatics, 2017, 13(5): 2214-2226.
[18] SHI X Y, XU Y L, GUO Q L, et al. A distributed EV navigation strategy considering the interaction between power system and traffic network [J]. IEEE Transactions on Smart Grid, 2020, 11(4): 3545-3557.
[19] LIU H, YAN J, GE S Y, 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 (in Chinese).
[20] HAIDAR A M A, MUTTAQI K M, SUTANTO D. Technical challenges for electric power industries due to grid-integrated electric vehicles in low voltage distributions: A review [J]. Energy Conversion and Management, 2014, 86: 689-700.
[21] ZHANG Y X, JIANG B, YAN H G, et al. Distributed power control considering different behavioural responses of electric vehicle drivers in photovoltaic charging station [J]. Journal of Shanghai Jiao Tong University (Science), 2019, 24(5): 597-604.
[22] FOTOUHI Z, HASHEMI M R, NARIMANI H, et al. A general model for EV drivers’ charging behavior [J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 7368-7382.
[23] PATERAKIS N G, ERDINC? O, CATAL?AO J P S. An overview of demand response: Key-elements and international experience [J]. Renewable and Sustainable Energy Reviews, 2017, 69: 871-891.
[24] QIAN T, SHAO C C, WANG X L, et al. Deep reinforcement learning for EV charging navigation by coordinating smart grid and intelligent transportation system [J]. IEEE Transactions on Smart Grid, 2020, 11(2): 1714-1723.
[25] LI J Q, XU X Y, YAN Z. A review of coupled electricity and hydrogen energy system with transportation system under the background of large-scale new energy vehicles access [J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 253-266 (in Chinese).
[26] PLATZER A. Verification of cyberphysical transportation systems [J]. IEEE Intelligent Systems, 2009, 24(4): 10-13.
[27] HE H W, WANG Y L, LI J W, et al. An improved energy management strategy for hybrid electric vehicles integrating multistates of vehicle-traffic information [J]. IEEE Transactions on Transportation Electrification, 2021, 7(3): 1161-1172.
[28] MORLOCK F, ROLLE B, BAUER M, et al. Forecasts of electric vehicle energy consumption based on characteristic speed profiles and real-time traffic data [J]. IEEE Transactions on Vehicular Technology, 2020, 69(2): 1404-1418.
[29] LI L, YANG M, GUO L D, et al. Hierarchical neighborhood based precise localization for intelligent vehicles in urban environments [J]. IEEE Transactions on Intelligent Vehicles, 2016, 1(3): 220-229.

Electric vehicle charging situation awareness for charging station ultra-short-term load forecast

基于充电态势感知的充电站负荷预测方法

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