This paper proposes a wireless power transmission (WPT)-based in-wheel switched reluctance motor (IWSRM) drive system, which uses an x-type converter at themotor side with charging function. In this system, a single f...
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This paper proposes a wireless power transmission (WPT)-based in-wheel switched reluctance motor (IWSRM) drive system, which uses an x-type converter at themotor side with charging function. In this system, a single frequency WPT subsystem with one transmitter coil and one receiver coil is used to completely isolate the DC power side from the motor side, and realize a transmission voltage gain of 1 by incorporating the symmetrical LCC-S network, which has the advantage of constant voltage gain when load changes. Be different from asymmetric half-bridge (AHB) converter, at the motor side an x-type converter with minimum number of active power switches is employed to connect the WPT subsystem, in which one power switch and one diode are used for each phase with independent control. The number of power active devices are reduced by half compared to AHB converter. Two freewheeling modes are analyzed for the drive system: the resistance freewheeling mode and the battery charging freewheeling mode. An experimental setup for a WPT-based three-phase 18/24-poleIWSRMsystemwas built and tested. The simulation and experiment results of the system under two freewheelingmodes are presented to validate the analysis and effectiveness of the system. The DC-to-DC efficiency is achieved over 96% with 100mmair gap and the system efficiency is achieved up to 82% with the battery charging freewheeling mode.
With the coming of the B5G and 6G era, lots of research reports held on predictions that the number of connected devices will keep exploding. According to specifications determined by the 3rd Generation Partnership Pr...
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ISBN:
(数字)9784885523397
ISBN:
(纸本)9784885523397
With the coming of the B5G and 6G era, lots of research reports held on predictions that the number of connected devices will keep exploding. According to specifications determined by the 3rd Generation Partnership Project (3GPP), when excess devices request internet may lead to signaling overhead of the charging system, it is necessary to predict the internet traffic required. Therefore, we take the advantage of meta-learning to effectively predict according to a few samples in the past. We implement the network data analytics function (NWDAF) and charging function based on meta-learning and implement it on a public cloud platform. Experimental results show that our proposed Meta-NWDAF architecture can reduce signaling significantly. Our research contributions are to show that meta-learning can be applied to not only classification problems but also time series prediction problems, and we also prove that the future diverse connected devices are well suited for leveraging me-ta-learning. The managerial implication of this research is that our proposed architecture effectively reduces the signaling overhead for the charging system.
In its "Sustainable and Smart Mobility Strategy", the European Commission assumes a 90% reduction in traffic emissions by 2050. The decarbonisation of transport logistics as a major contributor to climate ch...
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In its "Sustainable and Smart Mobility Strategy", the European Commission assumes a 90% reduction in traffic emissions by 2050. The decarbonisation of transport logistics as a major contributor to climate change is, therefore, indicated. There are major challenges in converting logistic transport processes to electric mobility. Currently, there is little available information for the conversion of entire fleets from fossil to electric fuel. One of the biggest challenges is the additional time needed for recharging. For the scheduling of entire logistics fleets, exact knowledge of the required loading times and loading quantities is essential. In this work, a parametrized continuous function is, therefore, defined to determine the essential parameters (recharging time, retrieved power, energy amounts) in HPC (high-power charging). These findings are particularly important for the deployment of multiple e-trucks in fleets, as logistics management depends on them. A simple function was constructed that can describe all phases of the charging process in a continuous function. Only the maximum power of the charging station, the size of the battery in the truck and the start SOC (state of charge) are required as parameters while using the function. The method described in this paper can make a significant contribution to the transformation towards electro-mobile urban logistics fleets. The required charging time, for example, is crucial for the planning and scheduling of e-logistics fleets and can be determined using the function described in this paper.
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