Realistic Traffic Data Based Mobility Modeling and Simulation of Smart EV

Kang, Chunyuan; Zhang, Tianle

doi:10.1109/dsc.2018.00137

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…To validate the feasibility and effectiveness of the proposed EV mobility model, and show how EV mobility affects the traffic system and the energy system, we set up a simulation scenario of a 3 km × 6 km campus area in Guangdong Province as the background with 500 digital twin EV nodes scatted randomly and 280 plugs of digital twin charging piles deployed. Each EV has been assigned to random initial configuration of parameters such as battery capacity, MPGe, maximum speed, and charging power [18,32]. A digital twin EV is automatically navigated according to the agenda tables and charging scheduling algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…To simulate the moving process of EVs, the dynamic position and direction of EVs should be calculated according to the mobility parameter and the EV profile [17]. Given the position of EV at time t is (x, y), the position and direction of EV at t + Δt (Δt is the interval slot of refreshing) can be obtained by a parametric equation as follows [18]:…”

Section: Motion Synthesis Modelmentioning

confidence: 99%

See 1 more Smart Citation

Time series behavior modeling with digital twin for Internet of Vehicles

Zhang¹,

Liu²,

Luo

et al. 2019

J Wireless Com Network

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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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Section: Methodsmentioning

confidence: 99%

Section: Motion Synthesis Modelmentioning

confidence: 99%

Time series behavior modeling with digital twin for Internet of Vehicles

Zhang¹,

Liu²,

Luo

et al. 2019

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, we divide one day into several periods, and then count the number of days that a user is in a certain state at a certain time based on the charging history. For example, by analyzing the charging history of the user in the past month (30 days), we can detect the charging intention of the user at T-time [24]. It can be found that in the past month, the number of days that the user is in state S 1 at T-time is 5 days (< T, S 1 > = 5), the number of days in state S 2 is 5 days (< T, S 2 > = 5), and the number of days in state S 3 is 20 days (< T, S 3 > = 20).…”

Section: Intention Detection Modulementioning

confidence: 99%

A method of chained recommendation for charging piles in internet of vehicles

et al. 2020

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With the popularization of new energy electric vehicles (EVs), the recommendation algorithm is widely used in the relatively new field of charge piles. At the same time, the construction of charging infrastructure is facing increasing demand and more severe challenges. With the ubiquity of Internet of vehicles (IoVs), inter-vehicle communication can share information about the charging experience and traffic condition to help achieving better charging recommendation and higher energy efficiency. The recommendation of charging piles is of great value. However, the existing methods related to such recommendation consider inadequate reference factors and most of them are generalized for all users, rather than personalized for specific populations. In this paper, we propose a recommendation method based on dynamic charging area mechanism, which recommends the appropriate initial charging area according to the user's warning level, and dynamically changes the charging area according to the real-time state of EVs and charging piles. The recommendation method based on a classification chain provides more personalized services for users according to different charging needs and improves the utilization ratio of charging piles. This satisfies users' multilevel charging demands and realizes a more effective charging planning, which is beneficial to overall balance. The chained recommendation method mainly consists of three modules: intention detection, warning levels classification, and chained recommendation. The dynamic charging area mechanism reduces the occurrence of recommendation conflict and provides more personalized service for users according to different charging needs. Simulations and computations validate the correctness and effectiveness of the proposed method.

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