• Journal of Communications and Networks
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• v.14 no.6
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• pp.662-671
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• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• Journal of Contemporary Eastern Asia
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• v.19 no.1
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• pp.43-58
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• 2020

This study examines media used for information in the East Asian countries of China, Japan, South Korea, Taiwan, and Singapore, using data from the World Values Survey. The sharing of Confucian culture may lead to a uniform media structure across these nations. Another possibility is technological determinism, which would also lead to similarity across nations. However, it is possible that countries are at different stages of technology development and will eventually become more similar. An opposing notion is that differences in other values among nations predict digital media use. To examine the evidence considering these possibilities, we factor analyze each population's use of nine traditional and digital media to see how similar the structures are. What results is a three-dimensional solution for four out of five countries, except Singapore, which has a more simple two-dimensional structure. Analysts regard Singapore as the most digitally connected society, which raises the question as to whether it is higher on a technological development trajectory, to which other countries may transition. Perhaps a more simple media use structure is an adaptation to increasing information load. As well, as mobile devices have become a primary means of accessing the range of traditional and social media, it may have an expanded role in reducing media channel entropy. In terms of frequency of media use, Singapore is highest, while China is the lowest. Singapore stands out in high mobile use, and China for low Internet use. There appear to be developmental differences across the nations. Regressions on Internet use for 18 values indices find different values predictors in the East Asian countries, ruling out Confucianism as producing similar media patterns.

• Transactions on Electrical and Electronic Materials
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• v.17 no.2
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• pp.57-68
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• 2016

In this study, we present the modern hybrid system based power generation for electric vehicle applications. We describe the hybrid structure of modified current source based DC - DC converters used to extract the maximum power from Photovoltaic (PV) and Fuel Cell system. Due to reduced dc-link capacitor requirement and higher reliability, the current source inverters (CSI) better compared to the voltage source based inverter. The novel control strategy includes Distributed Maximum Power Point Tracking (DMPPT) for photovoltaic (PV) and fuel cell power generation system. The proposed DC - DC converters have been analyzed in both buck and boost mode of operation under duty cycle 0.5>d, 0.5<d<1 and 0.5<d for capable electric vehicle applications. The proposed topology benefits include one common DC-AC inverter that interposes the generated power to supply the charge for the sharing of load in a system of hybrid supply with photovoltaic panels and fuel cell PEM. An improved control of Direct Torque and Flux Control (DTFC) based induction motor fed by current source converters for electric vehicle.In order to achieve better performance in terms of speed, power and miles per gallon for the expert, to accepting high regenerative braking current as well as persistent high dynamics driving performance is required. A simulation model for the hybrid power generation system based electric vehicle has been developed by using MATLAB/Simulink. The Direct Torque and Flux Control (DTFC) is planned using Xilinx ISE software tool in addition to a Modelsim 6.3 software tool that is used for simulation purposes. The FPGA based pulse generation is used to control the induction motor for electric vehicle applications. FPGA has been implemented, in order to verify the minimal error between the simulation results of MATLAB/Simulink and experimental results.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.143-151
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• 2018

In this paper, a novel hybrid configuration combining a phase-shift full-bridge (PSFB) and a half-bridge resonant LLC converter is proposed for the On-Board Charger of Electric Vehicles (EVs). In the proposed converter, the PSFB converter shares the lagging-leg switches with half-bridge resonant converter to achieve the wide ZVS range for the switches and to improve the efficiency. The output voltage is modulated by the effective-duty-cycle of the PSFB converter. The proposed converter employs an active reset circuit composed of an active switch and a diode for the transformer which makes it possible to achieve zero circulating current and the soft switching characteristic of the primary switches and rectifier diodes regardless of the load, thereby making the converter highly efficient and eliminating the reverse recovery problem of the diodes. In addition an optimal power sharing strategy is proposed to meet the specification of the charger and to optimize the efficiency of the converter. The operation principle the proposed converter and design considerations for high efficiency are presented. A 6.6 kW prototype converter is fabricated and tested to evaluate its performance at different conditions. The peak efficiency achieved with the proposed converter is 97.7%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.