LLC Resonant Converter Topologies For Plug-In Electric Vehicle Battery Charging

Recent improvements in battery technology and reduction in price have intensified interests in electrical vehicles (EVs) as these provide best means for pollution free and efficient transportation necessary for the sustainable development of the whole world. In near future, plug-in electrical vehicles (PEVs), which are equipped with on-board chargers, are expected to dominate the automobile market. Most commonly used on-board chargers consist of two stages with AC/DC converter as first stage and DC/DC converter as second stage. This thesis focuses on second stage whose function is to regulate charging voltage and current in accordance with battery’s charging requirements. The terminal voltage of EV battery varies over wide range during usage and it may discharge up to normally depleted or deeply depleted states. Therefore, the main challenge for DC/DC converter designer is to realize wide range of output voltage and current while maintaining good efficiency so that the converter is able to revive deeply depleted battery. To this end, this thesis contributes five novel topologies of LLC resonant converter for the DC/DC stage of on-board PEV battery charger which are: double LLC tank resonant converter, double LLC tank resonant converter with hybrid-rectifier, hybrid-bridge LLC resonant converter, hybrid-bridge LLC resonant converter with hybrid-rectifier, and interleaved LLC resonant converter with series connected voltage doublers. The first topology uses frequency control to achieve only depleted battery charging voltage range. Whereas, the other four topologies use mode changing with switching frequency control to extend the output voltage range for reviving deeply depleted battery, compared with conventional counterparts which use complex control techniques. Moreover, all the proposed topologies operate below resonance frequency for most extensively used normal battery charging range, therefore, power switches operate with ZVS and rectifier diodes with ZCS. The proposed topologies are designed for charging lithiumion PEV battery pack with terminal voltage as 420V when fully charged, 250V when depleted, and 100V or less when deeply depleted. The circuit configuration, analysis of operation, gain characteristics and design procedure of all the topologies are presented in details. Finally, all the proposed topologies are implemented and tested in laboratory and also simulated using MATLAB Simulink environment with 400V DC input and 1.5 kW maximum output power. The captured experimental and simulation results are presented in this thesis for validation of operation and performance of proposed converter topologies. The presented results showed that the four proposed topologies can effectively charge both normally depleted as well as deeply depleted battery, while the first topology can achieve only normally depleted battery voltage range. On the other hand, last two topologies have shown widest output voltage range of 50V–420V. Therefore, last two topologies have the ability to charge even very deeply depleted batteries. All the proposed topologies have peak efficiency higher than 95% at peak output power. However, the last topology which is interleaved LLC resonant converter with voltage doubler rectifier has highest efficiency of 95.65%. Moreover, this topology also has widest output voltage range of 50V–420V, so it can be considered as the best one among all the proposed topologies

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Feasibility, Architecture and Cost Considerations of Using TVWS for Rural Internet Access in 5G

The cellular technology is mostly an urban technology that has been unable to serve rural areas well. This is because the traditional cellular models are not economical for areas with low user density and lesser revenues. In 5G cellular networks, the coverage dilemma is likely to remain the same, thus widening the rural-urban digital divide further. It is about time to identify the root cause that has hindered the rural technology growth and analyse the possible options in 5G architecture to address this issue. We advocate that it can only be accomplished in two phases by sequentially addressing economic viability followed by performance progression. We deliberate how various works in literature focus on the later stage of this ‘two-phase’ problem and are not feasible to implement in the first place. We propose the concept of TV band white space (TVWS) dovetailed with 5G infrastructure for rural coverage and show that it can yield cost-effectiveness from a service provider’s perspective

IMPACT OF WORKPLACE ENVIRONMENT ON PERFORMANCE IN UNIVERSITY LIBRARIES OF LAHORE, PAKISTAN

Goals of this study were to know the effect of library’s work environment for job performance of librarians and existence of any relationship between work environment and job performance of librarians. A quantitative study design was carried out to explore phenomenon through the librarian’s perceptions, delights and choices about their administrative center surroundings. Survey method was used in this study. The target population of this study was Librarians of HEC recognized Public and Private Universities of Lahore City. The structured questionnaire was used for data collection from respondents. There had been a hundred and twenty questionnaires dispensed among librarians, the researcher became succeeded in receiving ninety-six questionnaires from members. The reaction rate was 80 percent. Descriptive statistics were utilized for description of information such as frequencies, mean scores and standard deviation. Regression analysis has been performed to measure the impact of workplace environment on the job performance. Findings of the study show that there is the shortage of calm environment to the employees. Job descriptions are not clearly designed. No proper training opportunities are provided to the workers. Employees are not properly motivated. Findings of the study may prove useful in providing a congenial environment to the employees so that performance may be improved

Effect of seed layer on the performance of ZnO nanorods-based photoanodes for dye-sensitized solar cells

In this paper, zinc oxide nanorods (ZnONRs) were synthesized by chemical bath deposition (CBD) method at 90 °C by using zinc nitrate hexahydrate and hexamethylenetetramine as precursors. In a first stage, the ZnO NRs were grown on un-seeded and pre-seeded fluorine-doped tin oxide (FTO) glass substrates by direct CBD method to study the effect of the ZnO seed layer on the NRs structural, morphological and optical properties. The X-ray diffraction (XRD) analysis performed on the preseeded NRs revealed a pure ZnO hexagonal wurtzite crystalline phase, while the Field Emission Scanning Electron Microscopy (FESEM) unveiled that the pre-seeded NRs exhibit a smaller diameter, higher density, higher aspect ratio and improved orientation along the c-axiswith respect to the un-seeded NRs. In a second stage, the powder obtained by aging, centrifuging and drying the precipitates formed during the CBD growth was analyzed by XRD to assess its crystal structure and phase purity and subsequently coated on un-seeded and pre-seeded FTO glass substrates by doctor blade technique. The ZnO NRs based seeded and non-seeded films fabricated by the two methods were finally used as photoanodes in dye-sensitized solar cells (DSSCs). Interestingly, the employment of pre-seeded ZnO NRs films deposited by doctor blade technique in comparison to the counterpart electrodes synthesized by direct CBD growth has led to a noticeable increase in the DSSC photoconversion efficiency from 0.35 to 1.86%. On the other hand, the inclusion of the seed layer has effectively improved the fill factor of the DSSC I-V curves for both the photoanode deposition techniques

Crowd Modeling using Temporal Association Rules

Understanding crowd behavior has attracted tremendous attention from researchers over the years. In this work, we propose an unsupervised approach for crowd scene modeling and anomaly detection using association rules mining. Using object tracklets, we identify events occurring in the scene, demonstrated by the paths or routes objects take while traversing the scene. Allen\u27s interval-based temporal logic is used to extract frequent temporal patterns from the scene. Temporal association rules are generated from these frequent temporal patterns. Our goal is to understand the scene grammar, which is encoded in both the spatial and spatio-temporal patterns. We perform anomaly detection and test the method on a well-known public data

Resource optimization‐based software risk reduction model for large‐scale application development

Software risks are a common phenomenon in the software development lifecycle, and risks emerge into larger problems if they are not dealt with on time. Software risk management is a strategy that focuses on the identification, management, and mitigation of the risk factors in the software development lifecycle. The management itself depends on the nature, size, and skill of the project under consideration. This paper proposes a model that deals with identifying and dealing with the risk factors by introducing different observatory and participatory project factors. It is as-sumed that most of the risk factors can be dealt with by doing effective business processing that in response deals with the orientation of risks and elimination or reduction of those risk factors that emerge over time. The model proposes different combinations of resource allocation that can help us conclude a software project with an extended amount of acceptability. This paper presents a Risk Reduction Model, which effectively handles the application development risks. The model can syn-chronize its working with medium to large‐scale software projects. The reduction in software failures positively affects the software development environment, and the software failures shall re-duce consequently. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

A Weighted Linear Combining Scheme for Cooperative Spectrum Sensing

AbstractCooperative spectrum sensing exploits spatial diversity of secondary-users (SUs), to reliably detect the availability of a spectrum. Soft energy combining schemes have optimal detection performance at the cost of high cooperation overhead, since actual sensed data is required at the fusion center. To reduce cooperation overhead, in hard combining only local decisions are shared; however the detection performance is suboptimal due to the loss of information. In this paper, a weighted linear combining scheme is proposed in which a SU performs a local sensing test based on two threshold levels. If local test result lies between the two thresholds then the SU report neither its local decision nor sequentially estimated unknown SNR parameter values, to the fusion center. Thereby, uncertain decisions about the presence/absence of the primary-user signal are suppressed. Simulation results suggest that the detection performance of the proposed scheme is close to optimal soft combining schemes yet its overhead is similar to hard combining techniques