Time series behavior modeling with digital twin for Internet of Vehicles
Electric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases,...
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| Vydané v: | EURASIP journal on wireless communications and networking Ročník 2019; číslo 1; s. 1 - 11 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Cham
Springer International Publishing
16.12.2019
Springer Nature B.V SpringerOpen |
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| ISSN: | 1687-1499, 1687-1472, 1687-1499 |
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| Abstract | Electric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases, carefully designed arrangement of resources and efficient utilization of the infrastructure is essential to the future development of EV industry. The mobility and distribution of EVs determine the charging demand and the load of power distribution grid. Then, dynamic traffic pattern of numerous interconnected EVs poses great impact on charging plans and charging infrastructure.
In this paper, we introduce the digital twin of a real-world EV by modeling the mobility based on a time series behaviors of EVs to evaluate the charging algorithm and pile arrangement policy. The introduced digital twin EV is a virtually simulated equivalence with same traffic behaviors and charging activities as the EV in real world. The behavior and route choice of EVs is dynamically simulated base on the time-varying driving operations, travel intent, and charging plan in a simulated large-scale charging scenario composed of concurrently moving EVs and correspondingly equipped charging piles. Different EV navigation algorithms and charging algorithms of Internet of Vehicle can be exactly evaluated in the dynamic simulation of the digital twins of the moving EVs and charging infrastructure. Then we analyze the collected data such as energy consumption, charging capacity, charging frequency, and waiting time in queue on both the EV side and the charging pile side to evaluate the charging efficiency. The simulation is used to study the relations between the scheduled charging operation of EVs and the deployment of piles. The proposed model helps evaluate and validate the design of the charging recommendation and the deployment plan regarding to the arrangement and distribution of charging piles. |
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| AbstractList | Electric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases, carefully designed arrangement of resources and efficient utilization of the infrastructure is essential to the future development of EV industry. The mobility and distribution of EVs determine the charging demand and the load of power distribution grid. Then, dynamic traffic pattern of numerous interconnected EVs poses great impact on charging plans and charging infrastructure.
In this paper, we introduce the digital twin of a real-world EV by modeling the mobility based on a time series behaviors of EVs to evaluate the charging algorithm and pile arrangement policy. The introduced digital twin EV is a virtually simulated equivalence with same traffic behaviors and charging activities as the EV in real world. The behavior and route choice of EVs is dynamically simulated base on the time-varying driving operations, travel intent, and charging plan in a simulated large-scale charging scenario composed of concurrently moving EVs and correspondingly equipped charging piles. Different EV navigation algorithms and charging algorithms of Internet of Vehicle can be exactly evaluated in the dynamic simulation of the digital twins of the moving EVs and charging infrastructure. Then we analyze the collected data such as energy consumption, charging capacity, charging frequency, and waiting time in queue on both the EV side and the charging pile side to evaluate the charging efficiency. The simulation is used to study the relations between the scheduled charging operation of EVs and the deployment of piles. The proposed model helps evaluate and validate the design of the charging recommendation and the deployment plan regarding to the arrangement and distribution of charging piles. Abstract Electric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases, carefully designed arrangement of resources and efficient utilization of the infrastructure is essential to the future development of EV industry. The mobility and distribution of EVs determine the charging demand and the load of power distribution grid. Then, dynamic traffic pattern of numerous interconnected EVs poses great impact on charging plans and charging infrastructure. In this paper, we introduce the digital twin of a real-world EV by modeling the mobility based on a time series behaviors of EVs to evaluate the charging algorithm and pile arrangement policy. The introduced digital twin EV is a virtually simulated equivalence with same traffic behaviors and charging activities as the EV in real world. The behavior and route choice of EVs is dynamically simulated base on the time-varying driving operations, travel intent, and charging plan in a simulated large-scale charging scenario composed of concurrently moving EVs and correspondingly equipped charging piles. Different EV navigation algorithms and charging algorithms of Internet of Vehicle can be exactly evaluated in the dynamic simulation of the digital twins of the moving EVs and charging infrastructure. Then we analyze the collected data such as energy consumption, charging capacity, charging frequency, and waiting time in queue on both the EV side and the charging pile side to evaluate the charging efficiency. The simulation is used to study the relations between the scheduled charging operation of EVs and the deployment of piles. The proposed model helps evaluate and validate the design of the charging recommendation and the deployment plan regarding to the arrangement and distribution of charging piles. Electric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases, carefully designed arrangement of resources and efficient utilization of the infrastructure is essential to the future development of EV industry. The mobility and distribution of EVs determine the charging demand and the load of power distribution grid. Then, dynamic traffic pattern of numerous interconnected EVs poses great impact on charging plans and charging infrastructure.In this paper, we introduce the digital twin of a real-world EV by modeling the mobility based on a time series behaviors of EVs to evaluate the charging algorithm and pile arrangement policy. The introduced digital twin EV is a virtually simulated equivalence with same traffic behaviors and charging activities as the EV in real world. The behavior and route choice of EVs is dynamically simulated base on the time-varying driving operations, travel intent, and charging plan in a simulated large-scale charging scenario composed of concurrently moving EVs and correspondingly equipped charging piles. Different EV navigation algorithms and charging algorithms of Internet of Vehicle can be exactly evaluated in the dynamic simulation of the digital twins of the moving EVs and charging infrastructure. Then we analyze the collected data such as energy consumption, charging capacity, charging frequency, and waiting time in queue on both the EV side and the charging pile side to evaluate the charging efficiency. The simulation is used to study the relations between the scheduled charging operation of EVs and the deployment of piles. The proposed model helps evaluate and validate the design of the charging recommendation and the deployment plan regarding to the arrangement and distribution of charging piles. |
| ArticleNumber | 271 |
| Author | Dong, Fuqiang Zhang, Tianle Liu, Xiangtao Luo, Zongwei Jiang, Yu |
| Author_xml | – sequence: 1 givenname: Tianle surname: Zhang fullname: Zhang, Tianle organization: Cyberspace Institute of Advanced Technology, Guangzhou University – sequence: 2 givenname: Xiangtao surname: Liu fullname: Liu, Xiangtao organization: Cyberspace Institute of Advanced Technology, Guangzhou University – sequence: 3 givenname: Zongwei surname: Luo fullname: Luo, Zongwei organization: Southern University of Science and Technology – sequence: 4 givenname: Fuqiang surname: Dong fullname: Dong, Fuqiang organization: Cyberspace Security Research Center, Peng Cheng Laboratory – sequence: 5 givenname: Yu orcidid: 0000-0002-6949-4876 surname: Jiang fullname: Jiang, Yu email: jiangyu@gzhu.edu.cn organization: Cyberspace Institute of Advanced Technology, Guangzhou University |
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| Cites_doi | 10.1016/j.comcom.2007.04.009 10.1016/j.ins.2019.04.011 10.1109/ISGTEUROPE.2010.5638947 10.1145/1161064.1161084 10.1016/j.adhoc.2006.05.012 10.1109/TII.2019.2938778 10.1109/MCOM.2007.4378332 10.1109/MWC.2008.4599222 10.1109/ITEC.2014.6861812 10.1109/TWC.2009.060598 10.1109/TVT.2019.2910217 10.1007/978-3-642-28487-8_21 10.3390/app9030437 10.1109/TPWRS.2009.2036481 10.1109/JIOT.2018.2846624 10.1109/TPWRD.2012.2236364 10.1109/JPROC.2010.2066250 10.1155/2018/7943586 10.1109/ACCESS.2018.2846590 10.1109/APEC.2011.5744627 10.3390/s18124486 10.1007/s11042-018-6938-9 10.1109/ACCESS.2018.2881422 10.1155/2018/5823439 10.1016/j.future.2018.12.054 10.1109/TII.2019.2907754 10.1109/DSC.2018.00137 10.3390/s18082440 |
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In Proc. of the SCS Western Multiconference Web-based Simulation Track, Jan. 1999 – reference: HanWeihongTianZhihongHuangZizhongLiShudongJiaYanBidirectional self-adaptive resampling in internet of things big data learningMultimedia Tools and Applications20187821301113012610.1007/s11042-018-6938-9 – reference: TianZGaoXSuSQiuJDuXGuizaniMEvaluating reputation management schemes of Internet of Vehicles based on evolutionary game theoryIEEE Trans Vehicular Technol20196865971598010.1109/TVT.2019.2910217 – reference: Toepfer, C.: SAE electric vehicle conductive charge coupler, SAE J1772. 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| SubjectTerms | Algorithms Behavior Charging scheduling Communications Engineering Computer simulation Data collection Digital twin Digital twins Driving Electric power distribution Electric vehicle Electric vehicle charging Electric vehicles Energy consumption Engineering Industrial development Information Systems Applications (incl.Internet) Infrastructure Internet of Vehicles Maintenance costs Mobility Modelling Multi-modal Sensor Data Fusion in Internet of Things Networks Queues Route selection Signal,Image and Speech Processing Stress concentration Time series Traffic planning |
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