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Cost efficient electric hybrid cycle
(AIKTC, 2026-06) Khan, Yusuf [Guide]; Khan, Zaid [22ME11]; Malik Abid [23DME12]; Khan, Tanveer [23DME10]
The design and development of a Solar Powered Electric Bicycle that uses solar energy along with battery power to drive the vehicle. Solar energy is a renewable, clean, and freely available energy source. By integrating a solar panel with an electric bike, the dependency on conventional charging methods can be reduced. The solar panel helps in charging the battery either partially or fully, depending on sunlight availability, thereby increasing the overall efficiency and sustainability of the vehicle. The proposed system mainly consists of a solar panel, rechargeable battery, PMDC motor, motor controller, throttle control, mechanical frame, wheels, and electrical wiring. The solar panel converts sunlight into electrical energy, which is used to charge the battery. The battery stores this energy and supplies power to the PMDC motor through the motor controller. The controller regulates the speed and torque of the motor based on the throttle input provided by the rider. A Permanent Magnet DC (PMDC) motor is used in this project due to its simple construction, high efficiency, good torque characteristics, and ease of speed control. PMDC motors are well suited for electric bike applications because they provide high starting torque and smooth operation at low speeds. The speed of the motor can be easily controlled by varying the voltage using the controller, which improves rider comfort and safety. The mechanical structure of the bike is designed to be strong, lightweight, and cost-effective.
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Smart power monitoring and optimization system (case study)
(AIKTC, 2026-06) Tadkod, Akshata [Guide]; Gaikwad, Pravin [21DEE10]; Mhetre, Ratan [21DEE21]; Shaikh, Mohammed Saquib [23DEE23]; Shaikh, Muskan [23DEE24]
This project focuses on the development of a smart power monitoring and optimization system based on an energy audit case study of industrial facilities. The primary objective is to analyze energy consumption, identify inefficiencies, and improve the overall performance of electrical systems. The study covers various subsystems including motors, pumps, transformers, and cooling systems using real-time and recorded operational data. The methodology involves data collection, performance evaluation, efficiency calculations, and identification of energy loss areas across different system components. The analysis revealed that several systems were operating under low load conditions and below optimal efficiency, resulting in significant energy wastage and increased operational costs. Based on the findings, suitable optimization techniques such as the implementation of Variable Frequency Drives (VFDs), load balancing of transformers, and replacement of inefficient equipment were recommended. These measures can significantly reduce energy consumption, improve system reliability, and support sustainable energy management practices in industrial environments. Energy Audit, Industrial Energy Management, Energy Conservation
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Contactless ev charging
(AIKTC, 2026-06) Patil, Pritika [Guide]; Bansode, Aniket [23DEE05]; Maske, Devang [23DEE06]; Meshram, Gaurav [23DEE13]; Nadgaonkar, Kaustubh [23DEE15]
The increasing global demand for sustainable transportation has accelerated the adoption of electric vehicles (EVs). However, the widespread use of plug-in charging systems poses challenges such as connector wear, user inconvenience, and safety hazards. To overcome these limitations, contactless or wireless electric vehicle charging has emerged as a promising alternative that enables energy transfer without physical connections. This project explores the design, working principles, and implementation of a stationary inductive wireless charging system based on the SAE J2954 standard. The system operates through electromagnetic induction between a ground-based transmitter coil and a receiver coil mounted under the vehicle. Power is transferred across a small air gap, converted from alternating current (AC) to direct current (DC), and delivered to the vehicle’s battery with high efficiency ( 90 A comprehensive literature survey was conducted to evaluate existing wireless power transfer (WPT) technologies, compensation topologies, alignment methods, and electromagnetic compatibility (EMC) considerations. Based on these findings, a proposed system was developed featuring power electronics, coil assemblies, alignment guidance sensors, and control mechanisms for safe and efficient energy transfer. The implementation details include system architecture, inverter design, coil alignment strategies, and protective circuitry to prevent overcurrent, overheating, and misalignment hazards. Results and research analysis confirm that wireless charging can achieve comparable efficiency to wired systems while enhancing convenience and safety. The project concludes that stationary contactless charging is a viable near-term solution for residential and shared parking facilities, while future work should focus on higher power transfer levels, dynamic charging (on-road), bidirectional energy flow, and cost optimization for large-scale deployment. Keywords: Wireless Power Transfer (WPT), Inductive Charging, Electric Vehicle (EV), SAE J2954, Electromagnetic Induction, Dynamic Charging, Resonant Coupling.
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Discrete wavelet transform approach for accurate fault diagnosis in three phase power system
(AIKTC, 2026-06) Tadkod, Akshata [Guide]; Ansari, Abdul Bari [22EE07]; Sayyed, Arbaaz [22EE12]; Khan, Mohammed Ruman [22EE20]; Shaikh, Zulkhairnain [22EE37]
This project presents the modeling and simulation of a transmission line system for fault detection using MATLAB Simulink. A 5-bus power system network is developed, consisting of a slack bus, multiple load buses, and interconnected transmission lines operating at 11 kV. The system is analyzed under normal and fault conditions to study the behavior of voltages and currents. Different types of faults such as line-to-line (L–L) and line-toground (L–G) faults are introduced at various buses using fault blocks. The impact of these faults on system parameters like current magnitude, voltage drop, and power flow is observed. Voltage and current measurement blocks are used to monitor real-time system performance. The results show that fault currents increase significantly during faults and vary depending on fault type and location. Line-to-ground faults produce unbalanced currents, while line-to-line faults affect multiple phases simultaneously. The simulation helps in understanding fault characteristics and provides a basis for designing protection schemes in power systems. This project demonstrates the effectiveness of simulation tools in analyzing transmission line faults and enhances understanding of power system stability and protection mechanisms. Keywords: Discrete Wavelet Transform (DWT), Fault Detection, item Transmission Line, MATLAB Simulink, 5 Bus System, Wavelet Coefficients, Current Signal Analysis, Transient Analysis,
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Ai-based wind turbine predictive maintainance
(AIKTC, 2026-06) Shaikh, Afzal [Guide]; Qureshi, Noaman [23DEE16]; Ramteke, Alok [23DEE17]; Sayed, Tuba [23DEE18]; Shaikh, Abdul Aziz [23DEE19]
Wind turbines play a key role in renewable energy generation, but their mechanical components— such as blades, bearings, and gearboxes—are prone to failures under extreme environmental stresses such as variable winds, vibrations, and temperature fluctuations. These breakdowns result in significant operational downtime, escalating maintenance costs, and reducing overall energy efficiency. This project introduces an innovative AIdriven predictive maintenance framework to mitigate these challenges by enabling early fault detection and proactive interventions. The system processes multi-modal sensor data, including vibration signals from accelerometers, torque measurements from transducers, and acoustic emissions from microphones, to identify incipient issues like bearing degradation or blade fissures prior to major disruptions. The methodology integrates advanced signal processing for time-frequency feature extraction (e.g., RMS, FFT, and wavelet transforms) with a hybrid AI model. This combines machine learning algorithms— Random Forest for non-linear pattern recognition and XGBoost for optimized gradient boosting—with deep learning via CNN-LSTM networks to capture spatial and temporal dependencies in time-series data. A real-time interactive dashboard, built on modular software pipelines, visualizes turbine status, generates predictive insights, and issues prioritized maintenance alerts to technicians.The anticipated results include a fault detection accuracy that exceeds 90%, along with a 30–40 percentage decrease in unplanned downtime compared to traditional scheduled maintenance approaches. By improving reliability and minimizing economic losses, this solution advances sustainable wind energy operations, offering a scalable blueprint for industry-wide adoption.