TIET Digital Repository

Thapar Institute of Engineering & Technology (TuDR)

Welcome to Thapar Institute of Engineering & Technology Digital Repository (TuDR).

TuDR is the digital asset management system which integrates the intellectual output in the form of research articles, PhD theses, and M.Tech / M.E. theses. TuDR facilitates the sharing and exchange of intellectual output of the university.

TuDR supports the management of scholarly resources of enduring value to Thapar University. Faculty members, students, and research scholars use TuDR services to share their intellectual work with the global academic community.

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  • For creating new Communities or Collections, mail to dspace@thapar.edu

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Now showing 1 - 5 of 8

Recent Submissions

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    Seminar on Multiaxial Behavior of Concrete
    (1984-11-30) Singh, Kanwal Jit; Kukreja, C. B.
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    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.
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    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.
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    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\%.