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The design of an optimized thermal management system for Li-ion batteries has challenges because of their stringent operating temperature limit and thermal runaway, which may lead to an explosion. In this paper, an optimized cooling system is proposed for kW scale Li-ion battery stack. A comparative study of the existing cooling systems; air cooling and liquid cooling respectively, has been carried out on three cell stack 70Ah LiFePO4 battery at a high discharging rate of 2C. It has been found that the liquid cooling is more efficient than air cooling as the peak temperature of the battery stack gets reduced by 30.62% using air cooling whereas using the liquid cooling method it gets reduced by 38.40%. The performance of the liquid cooling system can further be improved if the contact area between the coolant and battery stack is increased. Therefore, in this work, an immersion-based liquid cooling system has been designed to ensure the maximum heat dissipation. The battery stack having a peak temperature of 49.76 °C at 2C discharging rate is reduced by 44.87% to 27.43 °C after using the immersion-based cooling technique. The proposed thermal management scheme is generalized and thus can be very useful for scalable Li-ion battery storage applications also.

The rapidly increasing demand for energy storage has been consistently driving the exploration of different materials for Li-ion batteries, where the olivine lithium-metal phosphates (LiMPO4) are considered one of the most potential candidates for cathode-electrode design. In this context, the work presents an extensive comparative theoretical study of the electrochemical and electrical properties of iron (Fe)-, cobalt (Co)-, manganese (Mn)-, chromium (Cr)-, and vanadium (V)-based LiMPO4 materials for cathode design in lithium (Li)-ion battery applications, using the density-functional-theory (DFT)-based first-principle-calculation approach. The work emphasized different material and performance aspects of the cathode design, including the cohesive energy of the material, Li-intercalation energy in olivine structure, and intrinsic diffusion coefficient across the Li channel, as well as equilibrium potential and open-circuit potential at different charge-states of Li-ion batteries. The results indicate the specification of the metal atom significantly influences the Li diffusion across the olivine structure and the overall energetics of different LiMPO4. In this context, a clear correlation between the structural and electrochemical properties has been demonstrated in different LiMPO4. The key findings offer significant theoretical and design-level insight for estimating the performance of studied LiMPO4-based Li-ion batteries while interfacing with different application areas.

This paper demonstrates a smart energy management scheme for solar photovoltaic-biomass integrated grid-interactive microgrid cluster system. Three interconnected microgrids were chosen as a cluster of microgrids for validation of the proposed community energy management scheme. In this work, a Global System for Mobile (GSM)-based bidirectional communication technique was adopted for real-time coordination among the renewable energy sources and loads. To realize the common phenomenon of local grid outage in rural distribution networks, a practical case study is designed in this work. The optimized scheduling of the energy sources and loadsof different microgrids and the distribution grid were implemented to ensure zero loss of power supply probability (LPSP) for dynamic load profiles. The laboratory-scale prototype of the proposed microgrid clustering was first developed in this work by establishing real-time communication among multiple energy sources and loads through different energymeters located at different places inside the academic campus. The field validation was performed with a microgrid cluster consisting of 45 kWP solar photovoltaic, 50 kVA biogas plant, community loads in a village. The developed smart energy management solution is a generalized one and applicable to satisfy scalable community energy demands as well.

Background: Since the introduction of competency-based medical education in the medical curriculum all over India, emphasis has been given not only toward cognitive but also skill development of the undergraduate learners of the MBBS course. The traditional teaching methods are modified encouraging more interactive sessions, self-directed learning, and early clinical exposures. The assessment methods have also experienced certain modifications. For evaluation of the practical knowledge of the students in pre- and paraclinical departments, objective structured practical examination (OSPE) is being employed in several medical institutions. However, the question of its superiority and acceptability in comparison with the conventional practical examination exists there. Aims and Objectives: The present study aimed to estimate the perception and acceptance among the students regarding these two assessment methods. Materials and Methods: The 1st year undergraduate students of the Malda Medical College, Malda, were chosen for the study. After their prior sensitization regarding the two assessment methods, the students were given a feedback questionnaire having 15 questions made from different angles to compare the two modes of assessment. The results are subsequently analyzed. Results: The students (98%) marked OSPE as a more objective way of assessment, which also helps improve practical skills and confidence. They (95%) also recommended the implementation of OSPE in their practical examination. Conclusion: The majority of the students opined positively in favor of OSPE and stated their acceptance and recommendation in this respect.

Rapid fluctuation of solar irradiance due to cloud passage causes corresponding variations in the power output of solar PV power plants. This leads to rapid voltage instability at the point of common coupling (PCC) of the connected grid which may cause temporary shutdown of the plant leading to non-availability of energy in the connected load and distribution grid. An estimate of the duration and frequency of this outage is important for solar energy generators to ensure the generation and performance of the solar power plant. A methodology using PVsyst (6.6.4, University of Geneva, Geneva, Switzerland) and PSCAD (4.5, Manitoba HVDC Research Centre, Winnipeg, MB, Canada) simulation has been developed to estimate the duration and frequency of power outages due to rapid fluctuation of solar irradiance throughout the year. It is shown that the outage depends not only on the solar irradiance fluctuation, but also on the grid parameters of the connected distribution grid. A practical case study has been done on a 500 kilo Watt peak (kWp) solar PV power plant for validation of the proposed methodology. It is observed that the energy non-availability for this plant is about 13% per year. This can be reduced to 8% by incorporating a solar smoother. A financial analysis of this outage and its mitigation has also been carried out.

The UK government has set a ban on the sale of new petrol and diesel cars and vans by 2030. This will create a shift to electric vehicles. which will present a substantial impact on the grid. Therefore, methods to reduce the charging station’s impact on the grid have to be developed. This paper’s objective is to evaluate how integrating solar and storage affects a charging station’s dependence on the grid. A photovoltaic electric vehicle charging station (PVEVCS) is first designed, and then four charging profiles are selected to assess the station through a simulation using MATLAB. The array produces 3257 MWh/yr which, on average, offsets 40% of the electric vehicle (EV) load experienced by the station. Furthermore, with the integration of storage, the dependence is further reduced by 10% on average. The system also exported energy to the grid, offsetting close to all the energy imported.

The rapidly increasing demand for energy storage has been consistently driving the exploration of different materials for Li-ion batteries, where the olivine lithium-metal phosphates (LiMPO[sub.4] ) are considered one of the most potential candidates for cathode-electrode design. In this context, the work presents an extensive comparative theoretical study of the electrochemical and electrical properties of iron (Fe)-, cobalt (Co)-, manganese (Mn)-, chromium (Cr)-, and vanadium (V)-based LiMPO[sub.4] materials for cathode design in lithium (Li)-ion battery applications, using the density-functional-theory (DFT)-based first-principle-calculation approach. The work emphasized different material and performance aspects of the cathode design, including the cohesive energy of the material, Li-intercalation energy in olivine structure, and intrinsic diffusion coefficient across the Li channel, as well as equilibrium potential and open-circuit potential at different charge-states of Li-ion batteries. The results indicate the specification of the metal atom significantly influences the Li diffusion across the olivine structure and the overall energetics of different LiMPO[sub.4] . In this context, a clear correlation between the structural and electrochemical properties has been demonstrated in different LiMPO[sub.4] . The key findings offer significant theoretical and design-level insight for estimating the performance of studied LiMPO[sub.4] -based Li-ion batteries while interfacing with different application areas.

There is an increased awareness on security everywhere and the use of wireless internet technologies for security and surveillance applications is increasing. The Surveillance applications vary from monitoring super markets to large coast line harbors and so on. The solutions currently available in the market are proprietary, expensive and very hard to deploy. The objective of this project is to overcome this complexity and to come up with a simple and scalable solution to stream video wirelessly using open source software and easily available devices. The recent trend in multimedia computing applications requires increasing bandwidth and computing power. As an integral part of multimedia computing, the efficiency of digital video processing is a very compelling issue. This research focuses on an efficient way to wirelessly stream video from any Standard video4linux compatible USB Web Camera which can be scaled to high-end surveillance cameras as well to a mobile user. All communication between the mobile user and the web camera will be performed using a web browser. A real-time video stream will be efficiently encoded and transmitted from the web camera over an 802.11 WLAN and displayed in a mobile user's browser. In this project the end users will be able to remotely view the live video stream using any standard web browser running on any device with a wired or wireless interface. We have proposed a novel video encoding scheme to efficiently transmit video frames to a remote browser. Based on link quality and signal strength, the web camera will adaptively modify the number of frames transmitted per second and the quality of a transmitted frame to reduce packet loss and efficiently saving bandwidth and present the end user with a glitch free video. Based on the available bit rate information and the pre-determined data model the rate-adaptation module uses 'Newton Raphson' method to determine the encoding parameters. The research also includes understanding of various video compression techniques and Real time video streaming methods. Understanding of some open source libraries such as FFmpeg, MPEG4IP and Live555 are also a part of the research.

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust accumulation etc. Hence, it is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. Machine learning (ML) models have gained importance and are widely used for prediction of solar power plant performance. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time. The performance of chosen ML algorithms is validated by field dataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, a complete test-bed framework has been designed for data mining as well as to select appropriate learning models. It also supports feature selection and reduction for dataset to reduce space and time complexity of the learning models. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting EML models. The proposed work is a generalized one and can be very useful for predicting the performance of large-scale solar PV power plants also.