Browsing by Author "Kashyap, Y."

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A review on hybrid source energy management strategies for electric vehicle
(John Wiley and Sons Ltd, 2021) Vishnu Sidharthan, V.; Kashyap, Y.; Castelino, R.
Electric and hybrid electric vehicle technology demonstrates a performance to replace the internal combustion engines (ICE) in the current scenario. It attracts attention with improved fuel or energy efficiency with lower emissions. The overall system performance depends on powertrains, types of energy sources, electro-electronic interfaces, and energy management strategies (EMS). Significant issues of battery-powered electric vehicles (EV) are effects on the range, battery life, EV performance, battery maintenance, and replacement cost. Hybrid power source system (HPSS) solves EV challenges to a large extent. A hybrid combination of battery and supercapacitor (SC) to power the EV enhances the overall performance and life of the vehicle. Learning and integrated-based EMSs are gaining attention, with their ability in accurate and fast response in power handling among various sources. This paper analyses various DC-DC converter topologies of HPSS and compares multiple EMS with recent developments. In the context of challenges involved in EVs and research gaps that are discussed in the paper, EMSs need to be enhanced. The EMSs must consider the inputs for varying driving behaviors, road traffic, load, and environmental conditions to assure the flexibility of EV among different users across the globe. This is achieved by the management of SC power available to support the vehicle during sudden power requirements and enabling it to recuperate braking energy to improve the energy efficiency throughout the trip. Lastly, recommending precise research directions to achieve the development and improvement of the EMS and power electronic interfaces. © 2021 John Wiley & Sons Ltd.
Adaptive intelligent hybrid energy management strategy for electric vehicles
(John Wiley and Sons Inc, 2023) Vishnu, S.P.; Kashyap, Y.; Castelino, R.V.
Electric vehicles (EVs) utilizing hybrid energy sources is a significant step toward a sustainable future in the transportation industry. The electric three-wheeler (3W) considered in the proposed work includes three sources—battery, supercapacitor (SC), and photo-voltaic (PV) panels. In battery electric vehicles (BEV), battery life cycle, energy efficiency, and performance are affected by variations in driving conditions that inhibit their wider adoption. The main focus of the proposed intelligent hybrid energy management strategy (IHEMS) is to enable the vehicle to adaptively manage and diminish the effects of load fluctuations due to varying conditions. IHEMS diverts the load fluctuations to the SC bank by ensuring an effective absolute energy sharing among the sources with a fuzzy logic control algorithm. PV energy is utilized to assist the battery during sunny days. Performance of the EMS in hybrid source EV is analyzed in MATLAB/SIMULINK environment with a combination of three different standard real-time driving profiles (NYCC, Artemis Urban, WLTP class-1). Proposed EMS reduces peak battery power by 20% and 14.35% and improves battery life by 16.4% and 11.4% compared to BEV and conventional EMS, respectively. This proves that the proposed EMS exhibits adaptive energy management irrespective of the driving conditions and ensures improved battery performance and longevity. © 2022 John Wiley & Sons Ltd.
Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles
(MDPI, 2023) Sidharthan, V.P.; Kashyap, Y.; Kosmopoulos, P.
The energy utilization of the transportation industry is increasing tremendously. The battery is one of the primary energy sources for a green and clean mode of transportation, but variations in driving profiles (NYCC, Artemis Urban, WLTP class-1) and higher C-rates affect the battery performance and lifespan of battery electric vehicles (BEVs). Hence, as a singular power source, batteries have difficulty in tackling these issues in BEVs, highlighting the significance of hybrid-source electric vehicles (HSEVs). The supercapacitor (SC) and photovoltaic panels (PVs) are the auxiliary power sources coupled with the battery in the proposed hybrid electric three-wheeler (3W). However, energy management strategies (EMS) are critical to ensure optimal and safe power allocation in HSEVs. A novel adaptive Intelligent Hybrid Source Energy Management Strategy (IHSEMS) is proposed to perform energy management in hybrid sources. The IHSEMS optimizes the power sources using an absolute energy-sharing algorithm to meet the required motor power demand using the fuzzy logic controller. Techno-economic assessment wass conducted to analyze the effectiveness of the IHSEMS. Based on the comprehensive discussion, the proposed strategy reduces peak battery power by 50.20% compared to BEVs. It also reduces the battery capacity loss by 48.1%, 44%, and 24%, and reduces total operation cost by 60%, 43.9%, and 23.68% compared with standard BEVs, state machine control (SMC), and frequency decoupling strategy (FDS), respectively. © 2023 by the authors.
Airborne Kite Tether Force Estimation and Experimental Validation Using Analytical and Machine Learning Models for Coastal Regions
(MDPI, 2022) Castelino, R.V.; Kashyap, Y.; Kosmopoulos, P.
Wind power can significantly contribute to the transition from fossil fuels to renewable energies. Airborne Wind Energy (AWE) technology is one of the approaches to tapping the power of high-altitude wind. The main purpose of a ground-based kite power system is to estimate the tether force for autonomous operations. The tether force of a particular kite depends on the wind velocity and the kite’s orientation to the wind vector in the figure-eight trajectory. In this paper, we present an experimental measurement of the pulling force of an Airush Lithium 12 (Formula presented.) kite with a constant tether length of 24 m in a coastal region. We obtain the position and orientation data of the kite 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. We propose a physical model (PM) using Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) deep neural network algorithms to estimate the tether force in the experimental validation. The performance study using the root mean square error (RMSE) method shows that the LSTM model performs better, with overall error values of 126 N and 168 N under steady and turbulent wind conditions. © 2022 by the authors.
Airborne Manoeuvre Tracking Device for Kite-based Wind Power Generation
(Springer Science and Business Media Deutschland GmbH, 2021) Castelino, R.V.; Kashyap, Y.
Airborne wind technology eliminates the structure costs and reaches higher altitudes for extracting the power from stronger winds. The main objective is to perform the aerodynamic test on the airfoil kite with lightweight, low-power wireless devices for better data reception. The kite maneuvers in eight shapes to deliver maximum power; therefore, the positioning device has to be low powered, low weight, and weatherproof, to avoid indeterminacy in airfoil flight at 200â€“300Â m altitude. The device consists of ultra-low-power TI CC1310 SimpleLink Sub-1Ghz wireless Microcontroller Unit (MCU), GPS Sensor, Inertial Measurement Unit (IMU) to find the speed, direction, longitude, latitude, and altitude, roll, pitch, and yaw at the ground station, for controlling the tethered wings autonomously. The base station receives data at 868Â MHz optimum frequency at 50 kbps data rate; the optimized frequency is estimated using a virtual toolbox and a field test. Â© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting
(MDPI, 2023) Dhake, H.; Kashyap, Y.; Kosmopoulos, P.
The rapid growth in the use of Solar Energy for sustaining energy demand around the world requires accurate forecasts of Solar Irradiance to estimate the contribution of solar power to the power grid. Accurate forecasts for higher time horizons help to balance the power grid effectively and efficiently. Traditional forecasting techniques rely on physical weather parameters and complex mathematical models. However, these techniques are time-consuming and produce accurate results only for short forecast horizons. Deep Learning Techniques like Long Short Term Memory (LSTM) networks are employed to learn and predict complex varying time series data. However, LSTM networks are susceptible to poor performance due to improper configuration of hyperparameters. This work introduces two new algorithms for hyperparameter tuning of LSTM networks and a Fast Fourier Transform (FFT) based data decomposition technique. This work also proposes an optimised workflow for training LSTM networks based on the above techniques. The results show a significant fitness increase from 81.20% to 95.23% and a 53.42% reduction in RMSE for 90 min ahead forecast after using the optimised training workflow. The results were compared to several other techniques for forecasting solar energy for multiple forecast horizons. © 2023 by the authors.
An Improved ResNet-50 Neural Network Design for PV Panel Image Classification
(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, A.; Kashyap, Y.; Sharma, A.
The increasing popularity of photovoltaic (PV) setups stems from their capacity to generate clean and cost-effective electricity. However, various factors can either totally or partially disrupt the production of PV panels. To address this challenge, this study proposes a ResNet-50 model with dynamically adjusted hyperparameters to classify real-time captured images of PV panels into efficient and non-efficient categories. The hyperparameter tuning within the ResNet-50 model is conducted across three distinct cases, revealing that the most optimal classification results are achieved with the following settings: 50 epochs, a learning rate of 0.001, and a batch size of 32. The highest weighted average metrics, including accuracy (96%), recall (96%), precision (97%), and F1-score (96%), were obtained under these settings. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
An integrated frequency domain decomposition and deep neural network approach for short-term PV power forecast
(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, A.; Kashyap, Y.; Rai, A.
Weather disturbances and atmospheric parameters significantly influence the fluctuations in PV power output, which in turn affect the stability of grid operations. The current study proposed short-term PV power forecasting based on appropriate cutoff frequency in frequency domain and artificial intelligence method. Initially, the actual PV power data are decomposed into the frequency domain, and optimal cutoff frequency is determined by minimizing the squared difference of correlation between the decomposed components. Subsequently, the PV power is separated into low-frequency components (LFC) and high-frequency components (HFC). Then, long short-term memory (LSTM) and light gradient boosting machine (LGBM) models are then employed to forecast the LFC and HFC PV power. The final forecast output is generated using various recombination methods. The proposed combined forecast model, LFC-LGBM + HFC-LGBM, based on frequency domain decomposition (FDD) and LGBM approach, demonstrates superior performance compared to models (LFC-LSTM + HFC-LSTM), (LFC-LGBM + HFC-LSTM), and (LFC-LSTM + HFC-LGBM). The best-performing model (LFC-LGBM + HFC-LGBM) achieves a MAE of 4.9420%, a RMSE of 7.1047%, and a correlation index (R) of 0.9734 for 15-min ahead timesteps. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Brushless DC hub motor drive control for electric vehicle applications
(Institute of Electrical and Electronics Engineers Inc., 2020) Vishnu Sidharthan, P.; Kashyap, Y.
Global warming, air pollution and sound pollution are major environmental problems faced by today's world. Environmental pollution and increasing cost of fuels had led to the importance of using alternate sources for transportation since Internal combustion (IC) engine vehicles carries more than 40% of the pollution. Battery electric vehicles (BEV) is an alternative which received high attention in transportation industry. This paper focus on the control of Brushless DC (BLDC) motor which is used to drive the Electric two-wheeler. Hardware implementation of the motor is developed and an Atmega 328 microcontroller is utilized to control the motor which is provides a speed control with current commutation. A 60V, 1000W BLDC hub Motor is used to meet the drive requirements. The mathematical modeling of BLDC motor is discussed with the mathematical equations. MATLAB Simulation is developed, and the closed loop speed control is done in Simulink. PWM pulses generated from the MCU as per the commutation sequence and loaded to control the switches of 3-phase inverter and in response to the driver inputs (start/stop, accelerator, brake) the motor speeds are dynamically varied. Â© 2020 IEEE.
Climatological Trends and Effects of Aerosols and Clouds on Large Solar Parks: Application Examples in Benban (Egypt) and Al Dhafrah (UAE)
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Dhake, H.; Kosmopoulos, P.; Mantakas, A.; Kashyap, Y.; El-Askary, H.; Elbadawy, O.
Solar energy production is vastly affected by climatological factors. This study examines the impact of two primary climatological factors, aerosols and clouds, on solar energy production at two of the world’s largest solar parks, Benban and Al Dhafrah Solar Parks, by using Earth observation data. Cloud microphysics were obtained from EUMETSAT, and aerosol data were obtained from the CAMS and assimilated with MODIS data for higher accuracy. The impact of both factors was analysed by computing their trends over the past 20 years. These climatological trends indicated the variations in the change in each of the factors and their resulting impact over the years. The trends were quantified into the actualised drop in energy production (Wh/m2/year) in order to obtain the impact of each factor. Aerosols displayed a falling trend of ?17.78 Wh/m2/year for Benban and ?44.88 Wh/m2/year for Al Dhafrah. Similarly, clouds also portrayed a largely falling trend for both stations, ?36.29 Wh/m2/year (Benban) and ?70.27 Wh/m2/year (Al Dhafrah). The aerosol and cloud trends were also observed on a monthly basis to study their seasonal variation. The trends were further translated into net increases/decreases in the energy produced and the resulting emissions released. The analysis was extended to quantify the economic impact of the trends. Owing to the falling aerosol and cloud trends, the annual production was foreseen to increase by nearly 1 GWh/year (Benban) and 1.65 GWh/year (Al Dhafrah). These increases in annual production estimated reductions in emission released of 705.2 tonne/year (Benban) and 1153.7 tonne/year (Al Dhafrah). Following these estimations, the projected revenue was foreseen to increase by 62,000 USD/year (Benban) and 100,000 USD/year (Al Dhafrah). Considering the geographical location of both stations, aerosols evidently imparted a larger impact compared with clouds. Severe dust storm events were also analysed at both stations to examine the worst-case scenario of aerosol impact. The results show that the realized losses during these events amounted to 2.86 GWh for Benban and 5.91 GWh for Al Dhafrah. Thus, this study showcases the benefits of Earth observation technology and offers key insights into climatological trends for solar energy planning purposes. © 2024 by the authors.
Cloud Classification in Sky Images using Deep Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2024) Archit; Kumar, A.; Kashyap, Y.
In this research endeavor, a thorough analysis of a 10-minute sky video sequence was conducted. The study commenced by extracting frames at a consistent rate of 30 frames per second, resulting in a dataset of 1200 cloud images. Following frame extraction, color image segmentation was applied to identify distinct color regions within each frame.The primary goal was to estimate the percentage of cloudy pixels within each frame. To achieve this, three pre-trained Convolutional Neural Network (CNN) models namely - VGG16, MobileNetV2, and ResNet50 - were employed for cloud detection and pixel classification. This three-model approach contributed to a comprehensive assessment of cloud cover in the images. Cloudy pixel percentage was calculated using both area-based and pixel count-based approaches, adding depth to the analysis. This holistic approach provided a nuanced perspective on cloud cover dynamics, with the results shedding light on the evolution of cloud cover over the video's duration.The report meticulously outlines the methodology employed, encompassing data preprocessing techniques, feature extraction, and model training parameters. It presents the findings of the classification process, including accuracy and performance metrics, and discusses insights gained from the analysis of results. This paper contributes to the scientific discourse by presenting a comprehensive framework for cloud analysis in video sequences, with practical implications for a range of disciplines. Â© 2024 IEEE.
Design and development of a ground-based kite steer controller for kite-based wind power generation
(Springer Science and Business Media Deutschland GmbH, 2025) Castelino, R.V.; Kumar, P.; Kashyap, Y.
Kite Power Systems, a class of Airborne Wind Energy Systems (AWES), are capable of harvesting high-altitude wind energy using tethered kites, offering substantial material and efficiency advantages over traditional wind turbines. This paper introduces a novel ground-based Kite Steer Controller (KSC), pivotal for optimizing kite trajectory and power generation. The proposed KSC incorporates a Roll-Pitch-Zone control method, enabling precise steering in figure-of-eight trajectories while maintaining operational efficiency under varying wind conditions, including turbulence. Unlike prior approaches, this study emphasizes a detailed force analysis of control lines, revealing that control forces account for 23% of total aerodynamic forces, and the KSC consumes only 20% of the total power generated during a cycle. Experimental field tests with a 12 m2 Leading Edge Inflatable kite validate the system’s performance, demonstrating robust control capabilities under both steady and turbulent winds. This research advances global efforts in renewable airborne wind energy by presenting a scalable, energy-efficient solution for autonomous kite control, addressing critical challenges in AWES design and deployment. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Enhancing High-Frequency PV Power Forecast Using Optimal Hyperparameter Setting inÂ LSTM
(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, A.; Kashyap, Y.; Nasar, R.
Solar energy plays a significant role in the worldâ€™s shift to renewable and sustainable energy. So, accurate forecasting techniques are essential for effective grid management and smooth integration into current energy infrastructures. Traditional solar forecasting approaches often encounter limitations in capturing the complex and nonlinear relationships inherent in solar power generation patterns. In response to these challenges, the present paper demonstrates the forecast analysis of high-frequency (HF) PV power components, which is obtained with the decomposition of actual PV power data. The focus of this paper is on the analysis of high-frequency PV power components as they exhibit high fluctuation. To capture this high fluctuation feature present in PV power, a moving average filter is applied to smooth the input data and potentially enhance the 60 min ahead forecasting performance using the long short-term memory (LSTM) model. The best-performing LSTM model has secured MAE= 1.114 % and RMSE = 2.608 % for 60Â min ahead forecast. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach
(MDPI, 2023) Kumar, A.; Kashyap, Y.; Kosmopoulos, P.
The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques. © 2022 by the authors.
Ensemble RDLR Architecture for Short-Term Solar Power Forecasting
(Institute of Electrical and Electronics Engineers Inc., 2024) Ayappane, H.; Kashyap, Y.
Given the drastic shift of global sentiment towards renewable energy, it becomes incredibly important to match supply with demand. However the highly variable nature of weather makes it difficult to accurately predict the output of a solar power plant. Through this paper, we will approach this problem by using an ensemble model consisting of both machine learning and neural networks (NN) as base models to forecast the amount of energy that needs to be produced by a solar plant over a short-term time horizon, which in our case will be 0 minute (immediate), 5 minute, 30 minute and 90 minute. Each base model is fine tuned to encourage high diversity and low correlation to improve prediction accuracy. The expected stability or generalization from RF-DNN combined with the memory retention capability of the LSTM network should provide an ideal predictor for time series forecasting of a stochastic process like weather. Â© 2024 IEEE.
Exploring the Potential of Kite-Based Wind Power Generation: An Emulation-Based Approach
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Castelino, R.V.; Kumar, P.; Kashyap, Y.; Karthikeyan, A.; Sharma K, M.; Karmakar, D.; Kosmopoulos, P.
A Kite-based Airborne Wind Energy Conversion System (KAWECS) works by harnessing the kinetic energy from the wind and converting it into electric power. The study of the dynamics of KAWECS is fundamental in researching and developing a commercial-scale KAWECS. Testing an actual KAWECS in a location with suitable wind conditions is only sometimes a trusted method for conducting research. A KAWECS emulator was developed based on a Permanent Magnet Synchronous Machine (PMSM) drive coupled with a generator to mimic the kite’s behaviour in wind conditions. Using MATLAB-SIMULINK, three different power ratings of 1 kW, 10 kW, and 100 kW systems were designed with a kite surface area of 2.5 m (Formula presented.), 14 m (Formula presented.), and 60 m (Formula presented.), respectively. The reel-out speed of the tether, tether force, traction power, drum speed, and drum torque were analysed for a wind speed range of 2 m/s to 12.25 m/s. The satellite wind speed data at 10 m and 50 m above ground with field data of the kite’s figure-of-eight trajectories were used to emulate the kite’s characteristics. The results of this study will promote the use of KAWECS, which can provide reliable and seamless energy flow, enriching wind energy exploitation under various installation environments. © 2023 by the authors.
Intelligent Power Allocation Strategy of Hybrid Source System in Solar Electric Vehicle
(Institute of Electrical and Electronics Engineers Inc., 2022) Vishnu Sidharthan, P.; Kashyap, Y.
Technological developments in Battery electric vehicles (BEV) gains the utmost attention in recent transportation scenarios. Internal Combustion Engine (ICE) vehicles are facing several issues due to their effects on the environment, fuel cost, and availability. This shifts the automotive trends towards Electric Vehicles (EV). However, BEV faces few problems on range anxiety and battery life depletion for varying driving conditions. Supercapacitor (SC) coupled with batteries is the right solution for these rising problems. This paper focuses on the energy management of a battery-SC Hybrid Source System for a Solar Electric Vehicle (SEV). SC handles sudden power variations during varying driving load demands and solar irradiance. The proposed fuzzy logic power allocation strategy achieves improved battery life, source performances, and reduced battery peak currents for different driving and environmental factors. MATLAB/Simulink simulation results verify the significance of the integration of SC by reducing the battery stresses. The proposed strategy improves the battery longevity by 43% and 20% compared to BEV and hybrid conventional strategy respectively for a Worldwide Harmonized Light-duty Vehicles Test Procedure (WLTP) driving profile. Different driving conditions are considered in this work with varying driving and environmental conditions to prove the effectiveness of the proposed strategy. Â© 2022 IEEE.
Investigation of performance and technical assessments of hybrid source electric vehicles under different locations and driving conditions
(Taylor and Francis Ltd., 2024) Sidharthan P, V.; Kashyap, Y.
Sustainable transportation is a significant concept followed by nations implementing Nationally Determined Contributions (NDCs) that reduce emissions and adapt to climate change impacts. Electric vehicle (EV) adoption has accelerated; however, a trade-off exists between EV adoption and EV batteries-Battery charging from the grid (conventional energy sources) and e-wastes from retired batteries deposited in landfills. Thus, EVs associated with renewable energy sources (RES) are an alternate solution. This paper proposes a hybrid source electric vehicle (HSEV) with a high energy-dense supercapacitor (SC) as the primary source and PV energy as the secondary source. An energy management algorithm (EMA) with a modified controller is implemented in a Matlab/Simulink environment. Analysis of HSEV under varying locations (Australia, India, and Scotland), driving profiles (WLTP class-1, IDC, and ECE), and driving times (daytime, nighttime) highlights the importance of the proposed EMA. Grid charging instants are reduced to 3 times per month in Australia under WLTP class-1 cycle employing PV energy. Moreover, SC degradation is least compared to the lithium-ion battery in a BEV (Battery Electric Vehicle), hence avoiding the chances of maintenance and replacements. The proposed HSEV exhibits improved performance compared to BEVs of a similar type under different locations, driving, and environmental conditions. © 2023 Taylor & Francis Group, LLC.
Laboratory-Scale Airborne Wind Energy Conversion Emulator Using OPAL-RT Real-Time Simulator
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Kumar, P.; Kashyap, Y.; Castelino, R.V.; Karthikeyan, A.; Sharma K, M.; Karmakar, D.; Kosmopoulos, P.
Airborne wind energy systems (AWES) are more efficient than traditional wind turbines because they can capture higher wind speeds at higher altitudes using connected kite generators. Securing a real wind turbine or a site with favorable wind conditions is not always an assured opportunity for conducting research. Hence, the Research and Development of the Laboratory Scale Airborne Wind Energy Conversion System (LAWECS) require a better understanding of airborne wind turbine dynamics and emulation. Therefore, an airborne wind turbine emulation system was designed, implemented, simulated, and experimentally tested with ground data for the real time simulation. The speed and torque of a permanent magnet synchronous motor (PMSM) connected to a kite are regulated to maximize wind energy harvesting. A field-oriented control technique is then used to control the PMSM’s torque, while a three-phase power inverter is utilized to drive the PMSM with PI controllers in a closed loop. The proposed framework was tested, and the emulated airborne wind energy conversion system results were proven experimentally for different wind speeds and generator loads. Further, the LAWECS emulator simulated a 2 kW, 20 kW, and 60 kW designed with a projected kite area of 5, 25, and 70 square meters, respectively. This system was simulated using the Matlab/Simulink software and tested with the experimental data. Furthermore, the evaluation of the proposed framework is validated using a real-time hardware-in-the-loop environment, which uses the FPGA-based OPAL-RT Simulator. © 2023 by the authors.
Li-ion Battery Energy Storage Management System for Solar PV
(Springer, 2024) Chaitrashree, C.N.; Kashyap, Y.; Sidharthan, P.V.
Battery storage has become the most extensively used Solar Photovoltaic (SPV) solution due to its versatile functionality. This chapter aims to review various energy storage technologies and battery management systems for solar PV with Battery Energy Storage Systems (BESS). Solar PV and BESS are key components of a sustainable energy system, offering a clean and efficient renewable energy source. A background study on existing ESS, its advantages, and issues are detailed with the vital role of battery energy storage technologies, specifically LiBs, their characteristics, and SoC estimation techniques. Further, the chapter highlights integrating Battery Management Systems (BMS) with PV and BESS to ensure the efficient and reliable operation of the energy storage system. The major research gap/challenge is related to the less consideration given in terms of power consumption reduction and cost minimization, which forms multiple objective problem-solving. Multi-objective optimization with type 2 fuzzy controllers can achieve these objectives. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.