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Recent Submissions

Design and Development of Charging Schemes for Light Electric Vehicles
(2026-01-22) Singh, Ajay; Badoni, Manoj; Mishra, Anjanee Kumar
In this research work, the design and implementation of various converter topologies integrated with dual energy sources for charging of light electric vehicles (LEVs) are presented. The topologies are broadly classified as unidirectional and bidirectional DC to DC converters. These converters are additionally classified into non-isolated, isolated, and bridgeless types. This work presents a novel architecture for an on-board charging (OBC) system that integrates dual energy sources, viz., single-phase AC grid and solar PV. The system employs a Modified Single-Ended Primary-Inductor Converter (SEPIC) converter topology to facilitate Light Electric Vehicle (LEV) charging. A diode bridge rectifier is used to convert AC to DC from the AC mains. An improved CC-CV control technique is developed to ensure robust operation of the converter, maintaining unity power factor (UPF) operation. In the event of a grid outage, an integrated solar photovoltaic (PV) system efficiently charges the LEV battery using a Maximum Power Point Tracking (MPPT) converter, adapting to varying environmental conditions. The Modified SEPIC converter manages LEV charging, emphasizing enhanced efficiency, low conduction losses, reduced component count, and high gain. The designed system offers soft-starting features of the BLDC drive in propulsion mode without using any current and voltage sensors on the motor side. The performance of the system is tested by using the MATLAB simulation and validated by a hardware prototype, the results prove the improved performance of the advanced charging methodology by the proposed converter. This work also proposes an efficient configuration for a solar-powered on-board charging system utilizing a coupled inductor and switched capacitor bidirectional high-gain DC to DC converter with Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) operations. The bidirectional power flow capability of an on-board charger (OBC) benefits utilities and enhances the functionality of light electric vehicles (LEVs). The design of an OBC consists of an active front-end converter (AFC) for bidirectional power flow and unity power factor (UPF) operations. A proposed coupled inductor bidirectional high-gain SEPIC converter and a switched-capacitor bidirectional high-gain ZETA converter are designed and developed for the DC-DC stage. The AFC restricts the THD of supply current within the limits specified in international standards. In the event of a grid outage, an integrated solar photovoltaic (PV) system efficiently charges the LEV battery using a Maximum Power Point Tracking (MPPT) converter, adapting to varying environmental conditions. In addition, the brushless DC (BLDC) motor is used as a traction motor in this work due to its unique features, such as high density, low cost, simple control, etc. The presented LEV with a charging system is simulated in the MATLAB/Simulink platform, and real-time validation is performed using the OPAL-RT platform. The results obtained through both the simulation and real-time prototype indicate the effectiveness of the developed charging schemes with the coupled inductor and switched capacitor converter. Moreover, it introduces the design and implementation of a high-efficiency bidirectional isolated integrated DC to DC converter intended for the optimal charging and discharging of Light Electric Vehicle (LEV) batteries, utilizing dual power sources. The proposed system supports both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) operations, ensuring stable performance even during grid voltage disturbances, including sags, swells, and outages. To enhance the robustness of the controller, an advanced mixed second-order–third-order generalized integrator (IMSTOGI) control algorithm is introduced to facilitate reliable operation of the Active Front-End Converter (AFC) under grid disturbances. During normal grid conditions, the converter ensures unity power factor (UPF) and constant current performance. In the event of a grid outage, an integrated solar photovoltaic (PV) system efficiently charges the LEV battery using a Maximum Power Point Tracking (MPPT) converter, adapting to varying environmental conditions. The functionality and power management strategy of the system are validated through real-time experiments, showcasing its effectiveness, reliability, and potential for seamless integration with the smart grids and renewable energy sources. Both simulation and experimental results from an OPAL-RT prototype support the system’s economic and operational advantages, confirming the efficiency of the proposed advanced charging methodology with the isolated integrated converter. Additionally, this work introduces the design and implementation of a modified bridgeless SEPIC AC to DC converter topology with single-stage operations to facilitate LEV charging. The developed system utilizes two energy sources such as solar PV and single-phase grid. In the event of a grid outage, an integrated solar photovoltaic (PV) system efficiently charges the LEV battery using a Maximum Power Point Tracking (MPPT) converter, adapting to varying environmental conditions. The developed bridgeless converter manages LEV charging, with an emphasis on enhanced efficiency, low conduction losses, reduced component count, and high gain. The designed system offers soft-starting features of the BLDC drive in propulsion mode without using any current and voltage sensors on the motor side. The performance of the system is tested by using the MATLAB simulation and validated by hardware prototype, the results prove the improved performance of the advanced charging methodology by the proposed converter. This research presents an in-depth exploration of advanced DC-to-DC converter architectures integrated with dual power sources, namely solar photovoltaic (PV) systems and single-phase AC grid supply. The proposed solutions, which include modified SEPIC, bridgeless SEPIC, and high-gain bidirectional converters utilizing coupled inductors and switched capacitors, support both unidirectional and bidirectional power transfer—enabling efficient Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) functionality. Advanced control strategies such as Maximum Power Point Tracking (MPPT), Improved Mixed Second-Third Order Generalized Integrator (IMSTOGI), and Constant Current-Constant Voltage (CC-CV) ensure stable and efficient performance under varying grid and environmental conditions. The integration of smart grid capabilities alongside BLDC motor propulsion demonstrates the system’s flexibility. Simulation studies conducted in MATLAB/Simulink, along with real-time validation using the OPAL-RT platform, confirm the reliability, efficiency, and practicality of the proposed converter designs for Light Electric Vehicle (LEV) charging applications.
Design and development of frequency selective surfaces for wireless applications
(2026-01-19) Singh, Deepika; Joshi, Hem Dutt; Yadav, Rana Pratap
Frequency Selective Surfaces (FSSs) with multi-functional capabilities are a current research interest due to their wide range of applications, mainly in wireless communication, sensing, and radar systems. FSS plays an important role in RF systems within wireless communication networks, such as suppressing interference, enhancing transmission selectivity, improving channel quality, and directionally reflecting electromagnetic waves across various ranges. Considering current requirements, their importance becomes even more critical, especially for meeting communication bandwidth and transmission selectivity needs. This creates new opportunities and challenges to the researchers. This challenge is not just to enhance the existing structures but also adding various functionalities to the entire system keeping relatively low cost and maintaining efficiency. Various FSS designs have been extensively investigated with desirable properties like sharp roll offs, miniaturization, cost-effectiveness and reconfigurability. This thesis primarily focuses on the design and development of Frequency Selective Surfaces (FSSs) for wireless applications. The initial part of the thesis is dedicated in developing various passive FSSs for enhancing the desirable characteristics of the structure. Various techniques have been developed to achieve additional degrees of freedom in design parameters, cost efficiency, manufacturing feasibility and reliability. The advantages of 3-D printing and other low-cost substrate material have been utilized in prototyping different types of Frequency Selective Surfaces and investigating various desirable parameters, primarily to produce multiple bandwidth channels with intervals and sharp roll-off edges, which are highly anticipated in the development of reconfigurable FSS. The term "reconfigurable" refers to a wide range of parametric selectivity in FSS without physical changes to the structure. The reconfigurable FSS(RFSS) achieves a wider operating frequency range either tuning electrically or mechanically. In thesis work, RFSS incorporates active circuit elements, such as variable capacitors, to achieve real-time tuning of the resonating unit cells. Consequently, the development of reconfigurable FSS using active components is more challenging compared to conventional FSS. As a result, the work has been carried out in multiple stages. The desirable features of FSSs are explored and prototyping FSS using 3-D printing is a critical step, providing sufficient knowledge and data resources to finalize the design and viii implementation of reconfigurable FSS. Furthermore, the developed 3-D printed FSS and other low cost FSSs may have a wide range of applications, such as RF shields, reflectors, filters, etc. The key milestones of the work presented in this thesis are briefly discussed below: • The first part deals with two FSS designs to achieve higher selectivity and miniaturization characteristics. In first work, FSS is designed to exhibit filter like characteristics with flat passbands and fast roll-off edges, resulting in better frequency selectivity. Next design deals with miniaturization that led more and more unit cells to be integrated to smaller space thus saving size and space. The miniaturized FSS has been investigated using metallic vias to resonate at frequency bands of 1.24 GHz and 2.65 GHz. These works focus on achieving precise frequency control while maintaining lightweight, small and cost-effective designs. • The operating frequency of 3-D printed FSS has been altered by incorporating the designed elevated pattern on the surface of substrate. The work exploited the unit cell design by varying substrate height and metallization patterns and leads to significant variation in operating bands. The 3-D printed FSSs have also been explored for harmonic radar applications. The harmonic radar transmits at a harmonic frequency and detects the second harmonic frequency of reflected signal. The presented works reject the frequency at 2.5 GHz while passing the second harmonic frequency at 5 GHz frequency band. Another work is presented for RF shielding applications to suppress the various signals for security reasons and prevent cross-coupling between nearby wireless channels. The work also investigated the fabrication tolerances of 3-D printed technique. • The reconfigurable FSSs are explored for wideband tuning characteristics and beam steering applications. First work deals with dual bandstop tuning that can be individually as well as simultaneously tuned for achieving wideband characteristics. The wideband tuning with sharp roll off rejection at upper edge of frequency band is achieved by simultaneous varying the capacitance of varactor diode inserted at the top and bottom side of substrate. Effectiveness of the FSS design is tested by the fabricated prototype mounted with capacitors in order to achieve cost-effectiveness of proposed structure. Another reconfigurable bandpass FSS has been investigated to achieve desirable transmission phase for beam steering applications. The work mainly focuses on demonstrating the steering capability with extensive control over the phase distribution.
Enhancing Performance and Energy Optimization in Serverless Computing
(2026-01-07) Kaur, Jasmine; Chana, Inderveer; Bala, Anju
Serverless computing has been recognized as a transformative paradigm within cloud computing, offering Function-as-a-Service (FaaS) capabilities that allow developers to deploy applications without managing underlying infrastructure. Despite its advantages in scalability and cost-effectiveness, serverless computing still faces significant challenges related to workload unpredictability, inefficient resource utilization, energy consumption, and a lack of intelligent performance modeling. These issues are especially critical in serverless environments that demand dynamic autoscaling and precise workload management. This thesis presents a comprehensive study of performance modeling and energy optimization in serverless systems, focusing on autoscaling mechanisms based on learning-driven approaches. Initially, a detailed literature review has been conducted to investigate the performance metrics in serverless computing—such as response time, cost, energy consumption, cold start frequency, resource utilization, and fault tolerance—and to assess the limitations of existing autoscaling strategies. The findings emphasize the need for intelligent, adaptive autoscaling models to efficiently manage fluctuating workloads to enhance Quality of Service (QoS) adherence. The conventional approaches often fail to adapt effectively to sudden workload variations and lack the ability to learn from past performance data, which motivated the design of a more adaptive, learning-based autoscaling mechanism. Several models have been proposed and systematically evaluated throughout this research to address these concerns. Firstly, an auto-scalable model based on Q-learning has been introduced, enabling dynamic adjustment of compute resources in response to varying workload intensities. This model proves helpful in maximizing resource utilization by automatically scaling resources up or down as needed. The model continuously monitors incoming request rates and the current state of function instances, selecting scaling actions based on learned policies derived from historical performance data. The effectiveness of this model has been demonstrated on AWS Lambda, showing improvements in key metrics, including average response time reduced by 35.62\%, the mean number of idle instances minimized by 3.37\%, the probability of cold starts decreased by 38.5\%, and energy consumption lowered by 46.15\%. While the Q-learning–based autoscalable model improved performance and energy consumption, its single-agent nature limited scalability and hindered coordinated decision-making across distributed instances. To overcome this, a Multi-Agent Deep Q-Learning (MADQL) model has been proposed to overcome the limitations of single-agent methods by enabling cooperative learning among agents. This model effectively mitigates issues of overutilization and underutilization by allowing agents to make real-time scaling decisions. Through extensive experimentation on a real-world e-commerce dataset using AWS Lambda, significant improvements in metrics have been revealed, with average response time reduced by 0.96\%, cost lowered by 1.46\%, energy consumption minimized by 2.43\%, throughput increased by 0.44\%, and CPU utilization improved by 15.79\% compared with the existing model. Although MADQL provided cooperative learning and better workload distribution, it lacked predictive capabilities to anticipate workload surges, leading to reactive rather than proactive scaling. Building upon this, a hybrid learning model, LMP-Opt, has been introduced that integrates Long Short-Term Memory (LSTM) for workload prediction, Multi-Agent Deep Q-Learning (MADQL) for resource autoscaling, and Proximal Policy Optimization (PPO) for optimizing energy consumption through fine-tuning policy decisions. The LSTM component captures temporal workload patterns to facilitate predictive autoscaling. At the same time, MADQL dynamically allocates jobs by scaling resources up or down in response to workload fluctuations, and PPO has been introduced to refine these discrete actions into continuous ones, optimizing energy consumption and enhancing convergence. The proposed model has been further validated on AWS Lambda and ServerlessSimPro using dynamic e-commerce workloads, demonstrating improvements of up to 6.09\% in response time, 6.14\% in energy consumption, and 7.82\% in cost, while improving CPU utilization by 4.93\% and reducing the required number of nodes by 5.59\%.
Evaluation of Strength and Durability Properties of Cementitious Composites with Rice Stubble Biochar as Partial Binder Replacement
(Thapar Institute of Engineering and Technology, 2025-12-23) Kumar, Parshant; Roy, A B Danie; Goyal, Arpit
This study examines the impact of varying proportions of biochar produced from the pyrolysis of rice stubble waste on the strength and durability performance of cementitious composites. Ordinary Portland cement was partially replaced with finely ground biochar at replacement levels of 0%, 2.5%, 5%, 7.5%, and 10%. The resulting concrete mixes were prepared, cast, and cured under controlled conditions. The primary objective was to determine the optimal biochar dosage and evaluate the influence of biochar incorporation on fresh, mechanical, and durability characteristics of concrete through slump test, rebound hammer test, ultrasonic pulse velocity test, compressive strength test, splitting tensile strength test, flexural strength test, water absorption test, and rapid chloride permeability test. It was observed that increasing biochar concentration led to a progressive reduction in slump, indicating stiffer mixes due to the high porosity and water absorption capacity of the biochar particles. Compressive strength testing revealed that incorporating biochar enhanced the compressive strength, with a 7.5% replacement dosage emerging as the optimal dosage. However, higher dosages still yielded improved strengths relative to the control mix. In contrast, splitting tensile and flexural strengths decreased with increasing biochar content, attributed to the internal porosity introduced within the concrete matrix. Rebound hammer results exhibited agreement with the compressive strength trends, while ultrasonic pulse velocity outcomes similarly confirmed that M-3 (7.5% biochar) exhibited the highest pulse velocity, corresponding to its superior compressive strength. Rapid chloride permeability results further validated the enhanced performance of M-3, which demonstrated the lowest charge passed, indicating reduced chloride ion penetration. Conversely, water absorption showed an increasing trend with biochar content, with M-4 presenting the highest absorption value. Overall, it can be concluded that M-3, containing 7.5% biochar as partial cement replacement, represents the optimum mix composition, while higher dosages still provide improvements over the control mix in several aspects. This study highlights that incorporating biochar can promote matrix densification due to its fine particle size and filler effect; however, it may simultaneously increase overall porosity when introduced beyond the optimum dosage threshold.
Examining the Relationship Between Technology Business Incubators and Incubatees in Northern and Western Regions of India
(2025-12-23) Goyal, Abhimanyu; Kiran, Ravi
The measurement and optimisation of incubator performance is a contentious and regularly discussed issue among various stakeholders in business incubators. Numerous scholars have investigated various facets of business incubators, including their performance; yet, the literature remains deficient in certain areas. The performance of business incubators encompasses the performance of the incubatees, and both are mutually dependent for their survival and growth. The primary objective of the present study is to investigate the dyadic relationship between Technology Business Incubators (TBIs) and Incubatees. The study investigates the induction of the incubatees via the use of the selection criteria, different services and facilities offered by the business incubators to their incubatees, and the incubator sectoral differentiation. The present research empirically studies the different relationships between the exogenous (i.e., selection criteria, managerial skills, services, and facilities) and endogenous (i.e., incubator’s performance) constructs of the study. Using the Resource-Based View as the theoretical framework, it examines the influence of various incubator capital resource groups – organisational, human, and physical – on the sustained competitive advantage of the business incubators and how the different sub-resource groups affect their respective capital resource groups. The study uses a mix of descriptive, exploratory, and causal research designs. Out of 75 TBIs, respondents from only 34 expressed their willingness to participate in the study. One hundred responses were collected from the respondents – 41 from the incubator’s managerial team and 59 from the incubatees. Various measures of descriptive statistics and three inferential statistical techniques: Exploratory Factor Analysis (EFA), Partial Least Squares – Structural Equation Modelling (PLS-SEM), and Kruskal-Wallis test, were used to analyse the collated data. Through EFA, a varying number of sub-resources were identified for the different resource groups: four sub-resources for the organisational capital, i.e., incubator’s incubatee selection criteria; three for the human capital, i.e., incubator’s staff’s skills; ten for the physical capital, i.e., five each for incubator services and incubator facilities; and five for the sustained competitive advantage, i.e., incubator’s performance. Through PLS-SEM, empirical evidence was found that all the different incubator capital resource groups, to varying degrees, impacted the sustained competitive advantage of the business incubators, and various sub-resource groups also differently impacted their respective capital resource groups. Physical Capital in the form of Facilities (0.328) and Services (0.285) has the strongest influence on the business incubator’s sustained competitive advantage, followed by Organisational Capital (0.254) and Human Capital (0.232). Through the Kruskal-Wallis test, except for only two factors, i.e., Incubator’s Basic Facilities and Incubator’s Outreach Facilities, no sectoral differences were found. The study outcomes will provide valuable insights for the diverse stakeholders of TBIs and contribute to advancing theoretical understanding in this area. Using the study results, the researchers would be able to identify which sub-resource groups make higher and lower contributions to the overall strength of the construct within each resource group. Using the study’s findings, the incubation managers can streamline their incubation delivery and conserve the different scarce incubator capital resources. Using the study’s results, policymakers can strengthen existing programs or create new ones to support the continued skill development and capacity building of incubatees and incubation managers.