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The goal of this study is to look into how changes in crude oil prices affect GDP per capita and exchange rate fluctuations.to investigate the influence of crude oil price shocks on GDP per capita and exchange rate movements. This research employed yearly time series data for the price of crude oil, exchange rate (USD/INR), and GDP per capita, from 1990 to 2020. Arithmetical tools such as Descriptive, Unit Root, Granger Causality Test, and OLS Model were applied. The present study discovered a strong bi-directional Granger causality effect of Dubai crude oil prices on exchange rates, as well as a bi-directional Granger influence of exchange rates on WTI crude oil prices. The diagnostic tests were successfully passed by the estimated models. According to the OLS model, the exchange rate was driven only by the price of Dubai crude oil, although the price of WTI crude oil influenced both the GDP per capita and the exchange rate over the research period. The key policy recommendation derived from this analysis is that the Reserve Bank of India (RBI) must depreciate the rupee, first to restore much-needed exchange rate stability, then to stimulate domestic manufacturers, and finally, to attract foreign capital inflows.

With the increasingly obvious DC characteristics at both ends of the source and load sides of the low-voltage distribution network, the application scenarios of low-voltage DC microgrid gradually appear. Compared with the AC system, the DC microgrid has the characteristics of low inertia, weak damping, and poor anti-disturbance capabilities, all of which are important for improving operational stability. A virtual inertia adaptive control approach for fast-tracking energy storage under varied disturbances is presented using energy storage as a virtual inertia unit. Firstly, a stability analysis model including constant power load is constructed for the low-voltage DC microgrid; then, the control logic of the virtual inertia of the energy storage system is designed. Finally, the corresponding model is built in MATLAB/SIMULINK and the experiment platform to verify the correctness and effectiveness of the proposed control strategy.

In this paper, a two-stage three-port isolated bidirectional DC-DC converter (BDC) for hybrid energy storage system (HESS) applications in DC microgrids is proposed. It has an enlarged zero-voltage-switching (ZVS) region and reduced power circulation loss. A front-end three-phase interleaved BDC is introduced to the supercapacitor (SC) channel to compensate voltage variations of SC. Consequently, wide ZVS range and reduced circulation power loss for SC and DC bus ports are achieved under large-scale fluctuating SC voltage. Furthermore, a novel modified pulse-width-modulation (PWM) and phase-shift (PHS) hybrid control method with two phase-shift angles is proposed for BA port. And it contributes to an increasing number of switches operating in ZVS mode with varying battery (BA) voltage. Phase shift control with fixed driving frequency is applied to manage power flow. The ZVS range as well as the current stress of resonant tanks under varying port voltages is analyzed in detail. Finally, a 1 kW prototype with peak efficiency of 94.9% is built, and the theoretical analysis and control method are verified by experiments.

In this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight) in aggregate (TPA, TFA), and civilian vehicle number (CVN) and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015) based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA) and others is made to assess our task.

A serious issue relative to the construction of electronic devices is proper power source selection. This problem is of particular importance when we are dealing with portable devices operating in varying environmental conditions, such as military equipment. A serious problem in the construction of electronic devices is the correct selection of the power source. In these types of devices, lithium-ion batteries are commonly used nowadays, and in particular their variety—lithium iron phosphate battery—LiFePO4. Apart from the many advantages of this type of battery offers, such as high power and energy density, a high number of charge and discharge cycles, and low self-discharge. They also have a major drawback—a risk of damage due to excessive discharge or overcharge. This article studies the process of charging and discharging a battery pack composed of cells with different initial charge levels. An attempt was made to determine the risk of damage to the cells relative to the differences in the initial charge level of the battery pack cells. It was verified, whether the successive charging and discharging cycles reduce or increase the differences in the amount of energy stored in individual cells of the pack.

A detailed literature analysis depicts that artificial neural networks are rarely used for the power consumption estimation in the mobile robotics field. Instead, researchers prefer to develop analytical models of investigated robots. This manuscript presents a comparison of mathematical models and non-complex artificial neural networks in energy prediction tasks for differential and skid-steer drive robots which move over various types of surfaces. The results show that both methods could be used interchangeably but AI methods are more universal, do not depend on the kinematic structure of a robot and are tolerant for designers not having a complex knowledge about the system.

Protection of the natural environment is a key activity driving development in the transport discipline today. The use of simulators to train civil aviation pilots provides an excellent opportunity to maintain the balance between efficiency and limit the negative impact of transport on the environment. Therefore, we decided to determine the impact of selected simulations of air operations on energy consumption. The aim of the research was to determine the energy consumption of the flight simulator depending on the type of flight operation and configuration used. We also decided to compare the obtained result with the energy consumption of an aircraft of a similar class, performing a similar aviation operation and other means of transport. In order to obtain the results, a research plan was proposed consisting of 12 scenarios differing in the simulated aircraft model, weather conditions and the use of the simulator motion platform. In each of the scenarios, energy consumption was measured, taking into account the individual components of the simulator. The research showed that the use of a flight simulator has a much smaller negative impact on the natural environment than flying in a traditional plane. Use of a motion platform indicated a change in energy consumption of approximately 40% (in general, flight simulator configuration can change energy consumption by up to 50%). The deterioration of weather conditions during the simulation caused an increase in energy consumption of 14% when motion was disabled and 18% when motion was enabled. Energy consumption in the initial stages of pilot training can be reduced by 97% by using flight simulators compared to aircraft training.