Figure - available from: Wireless Communications and Mobile Computing
This content is subject to copyright. Terms and conditions apply.
Source publication
The penetration rate of electronic vehicles (EVs) has been increasing rapidly in recent years, and the deployment of EV infrastructure has become an increasingly important topic in some solutions of the Internet of Things (IoT). A reasonable balance needs to be struck between the user experience and the deployment cost of charging stations and the...
Similar publications
For the large-scale promotion of electric vehicles (EV), reliable fast-charging stations (FCS) demand high priority among EV users. However, unplanned locations of charging stations (CSs) and station capacity determination have adverse effects on the operation and the performance of power-distribution network. In this study, we developed an optimal...
Citations
... A dual ant colony optimization algorithm guided by heuristic information and pheromones to construct solutions for charging station siting and charging, respectively [7]. Uneven spatial and temporal distribution of electric vehicles in the city, particle swarm optimization algorithm-based electric vehicle charging station deployment strategy for a more rational siting strategy [8]. Establishing a genetic algorithm-based charging station site selection optimization model solves the problem of minimizing the operating cost under the constraints of charging station depreciation cycle, charging station power consumption per unit distance, and vehicle charging probability [9]. ...
To study the impact of traffic conditions of urban road networks and the distribution of potential demand of charging users on the charging distribution in the region. In this paper, the long short-time memory (LSTM) neural network is used to learn the historical user charging behavior data and predict the distribution of user charging behavior in real-time. An integer programming model for charging allocation of electric vehicles in the rolling time domain under time-varying demand is established by taking multiple charging stations and road networks in the region as research objects. Aiming at the highest comprehensive return, the model rejects some users with a long charging residence time before charging is allocated at the peak time and carries out the rolling time domain allocation of charging. When the demand for charging is high during peak charging hours, early rejection of long-staying users during the real-time allocation of charging appointments can increase the revenue of charging stations and the utilization rate of charging piles. In the case that the charging pile can be saturated at peak times, the allocation based on LSTM neural can increase the revenue of charging stations by 20.45% on average and the utilization rate of charging piles by 2.35% on average throughout the day. During peak hours, the revenue of the charging station can be increased by 36.63% on average, and the utilization rate of the charging pile can be increased by 13.54% on average.
... For example, Zhang et al. [21] first proposed a fast greedy algorithm to address the charging station siting problem by maximizing the charging demand, and then used M/M/N/N queuing theory to deal with the charging pile allocation problem by minimizing the queuing probability of EVs. Li et al. [22] proposed a PSO algorithm to deal with the two subproblems at the same time, where the objectives of the two subproblems are to minimize the operation cost and the time cost, respectively. Dai et al. [23] proposed an ascent heuristic algorithm (AHA) to deal with the two subproblems. ...
Charging infrastructure planning (CIPL) is key to popularizing electric vehicles and reducing carbon emissions. CIPL consists of two subproblems: charging station siting and charging pile allocation. The existing methods independently solve the two subproblems and ignore their interaction, which restricts the rationality of CIPL. To address this issue, this paper proposes a dual ant colony optimization for CIPL (DACO-CIPL). In each iteration, under the guidance of heuristic information and pheromones, the upper and lower ant colonies construct solutions for charging station siting and charging pile allocation in turn, respectively. Then, a global pheromone update strategy is performed to update the pheromones of each ant colony according to the historical best solutions, which realizes information transmission from the lower ant colony to the upper ant colony. In addition, whenever the upper ant colony finishes constructing solutions, a pheromone enhancement strategy is used to strengthen the pheromones of the lower ant colony according to the solutions of the upper ant colony, which realizes information transmission from the upper ant colony to the lower ant colony. DACO-CIPL is compared with several algorithms on multiple test instances. The experimental results show that DACO-CIPL has superior performance and more reasonable options for CIPL.
... rough social networks, learners can not only get the resources they need but also discover other relevant content, and then learn knowledge and wisdom from other channels or learners. In addition to providing teachers with basic knowledge, online teacher training is necessary to develop resources such as recommendation of excellent cases, presentation of famous teachers, and introduction of learning software and links of relevant content, as well as the construction of extended content other than training classes [23]. However, the current online training platforms for teachers are not enough to provide resources for expansion (as shown in Figure 3). ...
In order to expand the concept and form of teacher training, a remote human-computer interaction and STEM teacher online training platform based on the embedded Internet of Things is proposed. Based on the content analysis method, this article sorts out the development status of the current online teacher training platform and points out the shortcomings of the platform in information aggregation, resource construction, content setting, presentation, learning support services, and other aspects. Based on the combination of new concepts and new technologies, an online training platform for STEM teachers is proposed for the construction of the platform from four perspectives: innovative design concepts, enriching platform promotion ideas, developing educational makers, and improving support services, and the results of the use are investigated. The results show that the subjects believe that the STEM teacher online training system has clarified the STEM curriculum design steps, standardized STEM design methods, improved their cooperative learning ability, and cultivated design thinking. Among them, 13 people think that the training system has made STEM design steps clearer, “the design process is clearer,” “it makes me clearer about the whole design process,” and “the course design and development process is clearer.” Conclusion. This platform can provide a reference for online training and professional development of Chinese teachers.
... Chen et al. collected tens of thousands of residential travel and parking information, used regression analysis to predict demand, and established a mixedinteger programming model to plan the layout of stations [24]. Li et al. [25] also used the PSO to optimize the layout of EV charging stations to identify the number and location, which proved to be superior to the traditional k-means algorithm. Suryani et al. [26] created a simulation model for urban transportation planning based on system dynamics, performed a case study and drew conclusions. ...
The construction layout of charging piles restricts the rapidly development of EVs. To address this area, this paper first simulates the current layout of charging piles in Beijing and finds that there is an unbalanced distribution problem of oversupply and idle resources. Then, based on the current situation, this study takes the total charge station construction cost as the objective function and unfolds in three levels, using MIP for conditional constraints. Finally, an improved particle swarm algorithm is employed to simulate electric vehicles by generating demand points in the region to obtain the optimum location of charging stations. This method overcomes the limitation of using static data in traditional research, and the simulation can reflect the dynamic law of electric vehicle operation, which is more consistent with the real situation. The calculation results show that the method adopted in this study can reasonably plan the layout of charging stations, relieve the charging pressure of some charging stations, and minimize the overall service cost of new charging stations based on the continuation of the existing layout.
... Referring to the spatial unit division in the study on the impact of the built environment on travel behavior [26], all the above indicators, except for the charging pile density, take the 500 m buffer as the analysis unit. For the charging pile density, we set the buffer with a radius of 3 km as the analysis unit of the charging pile density according to the maximum distance that EVs can tolerate from the charging pile when they need to charge in the existing literature [28]. The selection and description of specific indicators are presented in Table 2. M i is the mixing entropy of land-use in buffer i, p ij is the proportion of the j-th species POI points in buffer i, and N i is the number of POI species in buffer i. ...
In this study, gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models were constructed to systematically ascertain the influencing factors and electric vehicle (EV) use action laws from the perspective of travelers. The use intensity of EVs was represented by electric vehicle miles traveled (eVMT); variables such as the charging time, travel preference, and annual income were used to describe the travel characteristics. Seven variables, including distance to the nearest business district, road density, public transport service level, and land use mix were extracted from different dimensions to describe the built environment, explore the influence of the travel behavior mode and built environment on EV use. From the eVMT survey data, points of interest (POI) data, urban road network data, and other heterogeneous data from Chongqing, an empirical analysis of EV usage intensity was conducted. The results indicated that the deviation of the GBDT model (9.62%) was 11.72% lower than that of the OLS model (21.34%). The charging time was the most significant factor influencing the service intensity of EVs (18.37%). The charging pile density (15.24%), EV preference (11.52%), and distance to the nearest business district (10.28%) also exerted a significant influence.
... Platform plays an intermediary role in VCC, and VCC has a significant impact on TDS value [50]. First, IS has a significant impact on TDS value. ...
The Internet of Things (IoT) has brought many benefits to the development of industries, from manufacturing to services. In the context of IoT, the network freight transport platform is equivalent to an integrator, integrating the scattered resources such as carriers and shippers in the supply chain, providing a solution, improving the use efficiency of vehicles, effectively reducing logistics cost, and creating value for the freight transport platform and bilateral customers. The purpose of this research is to explain the influence mechanism of transport demand subjects’ participation in value cocreation (VCC) under the service-dominant logic (SDL). We proposed a conceptual framework and 10 hypotheses and used SEM to measure the direct and mediating relationship among SDL, transport demand subject (TDS) participation, relational embeddedness (RE), platform VCC, and TDS value in the online business environment. The findings show that (1) SDL, TDS participation, and RE do not directly affect the TDS value, but indirectly through platform VCC; (2) SDL affects platform VCC not only directly but also indirectly through TDS participation and RE; (3) TDS participation affects platform VCC not only directly but also indirectly through RE; (4) RE indirectly affects TDS value by affecting platform VCC; and (5) platform VCC plays a mediating role in SDL, TDS participation, RE, and TDS value. Hence, transport enterprises should pay attention to the innovation concept, guide TDS participation, jointly improve platform VCC, give play to the network effect of the freight transport platform, and achieve TDS value through the deepening of TDS relationship embedding.
In this study, we uniformly partition an empirical vehicular network map and determine the locations where electric vehicle (EV) charging stations will be deployed by implementing the particle swarm optimization (PSO) algorithm. The optimal placements utilize the spatiotemporal taxi movements representing energy demands. From each partition, the taxi GPS coordinates are extracted and used to obtain the global best location of the charging station. Parameters such as the total number of taxis accessible to each deployed EV charging station and the average distance of each taxi from all the charging stations have also been computed to evaluate the deployment method. Results have shown that the total number of taxis within the 1-km radius distance of each EV charging station ranges from 797 to 1218 taxis, while the average distance of each taxi from all of the charging stations ranges from 3.14 to 4.46 km. We also note that there is a small inter-distance separation among all charging stations.
