Browsing by Author "Kashyap, Yashwant"

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Design and Development of Flight Controller for Kite-based Wind Power Generation Systems
(National Institute of Technology Karnataka, Surathkal, 2024) Castelino, Roystan Vijay; Kashyap, Yashwant
Wind power can significantly contribute to transitioning from fossil fuels to renewable energies. Airborne Wind Energy (AWE) technology is one of the approaches to tapping the power of high-altitude wind. Kite Power System (KPS) is a type of AWE technology which uses tethered kites to harness the power in the wind at higher altitudes. The main advantage of KPS over Conventional Wind Turbines (CWT) is that the KPS eliminates the need for a structure and rotating blades, significantly reducing the size and materials needed. Also, kites can reach much higher altitudes than CWT, which can harness the power from much stronger winds. The study of the dynamics of KPS is fundamental in researching and developing a commercial-scale system. Unlike CWTs, where the blades rotate in a circular motion, the kites are controlled to follow figure-eight trajectories in the crosswinds. The kites harness power from the wind and transfer the aerodynamic force through the tethers to the ground. The reeling-out tether rotates the generator at the ground station to generate electric power. As the tether length is finite, the kite is depowered and reeled in at the end of the limit by consuming a fraction of generated power. The cycle of operation repeats and is called as pumping cycle kite power system. KPS is one of the solutions contributing towards clean and green energy production in the renewable energy mix, which is the sole motivation of this research work. The KPS has challenges that must be addressed to develop it as a commercially viable product. The power from the kites depends on the tether force of the kite in the figure-eight trajectory. The tether force of a kite depends on the wind velocity and the kite’s orientation to the wind vector in the figure-eight trajectory. This research presents an experimental measurement of the pulling force of an Airush Lithium 12 m2 kite with a constant tether length of 24 m in a coastal region. The position and orientation data of the kite is obtained from the sensors mounted on the kite. The flight dynamics of the kite are studied using multiple field tests under steady and turbulent wind conditions. In this research, a physical model (PM), Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) deep neural network algorithms are proposed to estimate the tether force of the kite with experimental validation. The performance of the proposed methods is studied using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 evaluation methods. The potential of KPS can be realized by scaling the model to a commercialscale power generation device. Testing an actual KAWECS or a location with suitable wind conditions is only sometimes a trusted opportunity for conducting esearch. A KAWECS emulator is developed based on a Permanent Magnet Synchronous Machine (PMSM) drive coupled with a generator to mimic the kite’s behaviour in wind conditions. The KPS is simulated using a MATLAB-SIMULINK environment with various power ranges and wind conditions. The satellite wind speed data at 10 m and 50 m above ground with field data of the kite’s figureof-eight trajectories are used to emulate the kite’s characteristics in the dynamic wind conditions. Another challenge in the KPS is the steering controller, which controls and flies a kite in figure-eight crosswind motion. The kite consists of two power lines and two control lines, the power lines are connected together, and the kite delivers most of the force through the power lines. The left and right control lines are used to steer the kite by a differential movement of both lines. The kite steer controller mimics the differential movement of the kite control lines to steer the kite. The force exerted on the control lines by the kite is essential in designing the kite steering actuators. A kite steering controller’s design and development methodology is explained and validated using experimental analysis under steady and turbulent wind conditions. The power consumed to control the kite and the power generation aspects of the KPS is also analyzed. The results of this research will promote the use of KAWECS as it can provide reliable and seamless energy flow, enriching wind energy exploitation under various installation environments.
Intelligent Power Allocation Strategy for Electric Vehicles
(National Institute Of Technology Karnataka Surathkal, 2023) P, Vishnu Sidharthan; Kashyap, Yashwant
Transportation plays a significant role in the global economy, and its energy re- quirement has increased tremendously to reach 29 %, with massive growth in the past decade. Meanwhile, the transportation industry has consumed almost 2/3rd of oil demand and nearly 1/4th of global carbon dioxide emissions from fossil fuel combustion. Moreover, a hike in fossil fuel costs and their reduced availability are other major issues that motivated the development of a green and clean mode of transportation. In that context, vehicles with an alternative energy source are essential. This highly motivated the development of battery electric vehicles and hybrid electric vehicles. Researchers have focused on investigating innovations with EVs. Although EVs are developed with improved performance and comfort, certain is- sues hinder their wider adoption. The Lithium-ion battery is one of the primary energy sources for electrified transportation. The battery performance of BEVs is affected by variations in different driving profiles and conditions. Vital factors that decide the performance of the EVs are 1) Battery life, 2) Range or mileage 3) Battery capacity fade costs. Hence investigation of these parameters is necessary for analyzing BEVs performance. Considering the recent advanced BEV technologies, the performance parame- ters are not up to the mark. Battery life - Battery degradation affects the max- imum power handling capability of the BEV battery, thus leading to poor per- formance of the vehicle. It reduces the life cycle of the battery and may initiate battery replacement. Range anxiety - The EV’s range or mileage decides the user’s anxiety. A reduced range creates range anxiety while driving since the charging stations are not ubiquitous. Capacity fade cost - This is an after-effect of battery life depletion. However, these are not similar for all users. It differs based on different driving conditions such as 1) Vehicle (starting, velocity, acceleration, and braking), 2) Driver (driving behaviors, route selection, charging, and usage patterns), 3) Environment (irradiance, ambient temperature, wind speed, road, and traffic conditions). Whenever the battery’s capacity reaches less than 75%, it is called the battery’s end of life. The battery fade costs must be reduced to increase the popularity of BEVs. The solution to such impacts on the BEVs high- lights the usage of hybrid sources in EVs. A supercapacitor coupled with a battery handles the transient load current of the EV traction. The SC response time and power density are higher than batteries; thus, it can ensure battery safety. Moreover, the regenerative braking energy can be recuperated in the SC, which improves energy efficiency. The en- ergy utilization of the transportation industry is increasing tremendously. 2/3rd iiiof total energy consumption will be occupied by renewable energy (wind, solar, bioenergy, geothermal, and hydro energy) by the end of 2050. Electrified trans- portation combined with renewable energy sources reduces emissions by 60 %. Utilizing renewable sources in the vehicle ensures a green, clean, and sustainable transportation sector. Developing countries with higher solar insolation can em- ploy solar panels to charge the EV sources during the daytime. They can achieve their daily commute with less number of charging from the grid. Therefore the contribution of Hybrid Source Electric Vehicles (HSEV) will be a significant step toward a sustainable future. The SC and PV are the auxiliary sources coupled with the Lithium-ion bat- tery in a proposed hybrid source system in EVs. An Intelligent Hybrid Source Energy Management Strategy (IHSEMS) employing a fuzzy logic-based controller is successfully introduced to overcome the issues and drawbacks of the existing electric vehicle systems ensuring an optimal source operations. The proposed al- gorithm ensures absolute energy sharing among each source and diminishes the impact of varying driving and environmental conditions. The proposed energy management strategy for HSEV improves the battery charge levels, increases the vehicle range, eliminates high C-rate instants, avoids frequent charge and dis- charging (fluctuations) in battery current profile, and improves the battery life. Moreover a modified energy management algorithm (EMA) is proposed for a high energy-dense SC and high conversion efficient PV panels based on a new hybrid energy vehicle. Investigations on different locations with varying driving and en- vironmental conditions are conducted to highlight the significance of the proposed hybrid source model. A detailed techno-economic assessment shows the signifi- cance of the proposed hybrid models and respective proposed energy management algorithms compared to BEVs and existing EMSs. Moreover, in countries with underdeveloped grid infrastructure, Solar PV in electric vehicle applications can be highly beneficial.