DEPARTMENT OF MECHATRONICS ENGINEERING

The increasing complexity of Electric Vehicle (EV) powertrains necessitates a robust, integrated, and flexible control strategy, centralized within the Electric Vehicle Control Unit (EVCU). This study shows the implementation of the Id = control strategy using the MATLAB/Simulink Motor Control Blockset under varying loading conditions and speed requirements. This study further goes on to show an implementation of a CC-CV charging controller for a Li-ion battery with multiple current control loops. The study is designed for compliance with the AUTOSAR (Automotive Open System Architecture) Classic Platform for compliance – ensuring modularity, portability and adherence to industry standards. The study results validate the performance of the Interior PMSM and the ability to generate C implementation and header files from the model-based engineering (MBE) design approach which can be further used for hardware-in-the-loop testing. This study concludes that the MBE and AUTOSAR approach produces a highly efficient framework for developing, validating and iterating on complex, multi-domain electric vehicle components.

The integrity and efficient operation of pipelines are critical for the safe transportation of oil, gas, and other fluids. Traditional pipeline monitoring methods often fall short in providing comprehensive and real-time data essential for proactive maintenance and risk management. This project explores the integration of smart technologies in pipeline pigging systems to enhance pipeline monitoring and management. Smart pigging involves the use of intelligent inspection tools that traverse the pipeline, collecting high-resolution data on internal conditions, including corrosion, cracks, and other anomalies. By leveraging advanced sensors, data analytics, and real-time communication technologies, smart pigging systems offer unprecedented insights into pipeline health, enabling predictive maintenance and timely interventions. The implementation of these systems can significantly reduce the risk of leaks and ruptures, thereby ensuring environmental safety and operational efficiency. This study reviews the latest advancements in smart pigging technology, examines case studies of successful implementations, and discusses the challenges and future directions in the field of smart pipeline monitoring. In summary, the implementation of smart pipeline monitoring using pigging systems has delivered substantial benefits in terms of anomaly detection, maintenance optimization, operational efficiency, safety, and environmental protection. The device was fully tested and proven to perform optimally by taking temperature readings of the pipeline and converted it to pressure through the use of a programmable microcontroller. Pipeline leaks and failures can easily be detected by this prototype model when there is an increase and decrease in the flow rate of the fluid

The integrity and efficient operation of pipelines are critical for the safe transportation of oil, gas, and other fluids. Traditional pipeline monitoring methods often fall short in providing comprehensive and real-time data essential for proactive maintenance and risk management. This project explores the integration of smart technologies in pipeline pigging systems to enhance pipeline monitoring and management. Smart pigging involves the use of intelligent inspection tools that traverse the pipeline, collecting high-resolution data on internal conditions, including corrosion, cracks, and other anomalies. By leveraging advanced sensors, data analytics, and real-time communication technologies, smart pigging systems offer unprecedented insights into pipeline health, enabling predictive maintenance and timely interventions. The implementation of these systems can significantly reduce the risk of leaks and ruptures, thereby ensuring environmental safety and operational efficiency. This study reviews the latest advancements in smart pigging technology, examines case studies of successful implementations, and discusses the challenges and future directions in the field of smart pipeline monitoring. In summary, the implementation of smart pipeline monitoring using pigging systems has delivered substantial benefits in terms of anomaly detection, maintenance optimization, operational efficiency, safety, and environmental protection. The device was fully tested and proven to perform optimally by taking temperature readings of the pipeline and converted it to pressure through the use of a programmable microcontroller. Pipeline leaks and failures can easily be detected by this prototype model when there is an increase and decrease in the flow rate of the fluid

The growing demand for compact, high-performance electronic devices has intensified the challenge of managing heat generation, which can significantly affect system reliability, efficiency, and lifespan. This study focuses on the design and simulation of a mechatronic cooling system for electronic devices, integrating thermal, electrical, and control subsystems into a unified framework for intelligent temperature regulation. The primary aim was to develop a system capable of maintaining thermal stability and optimizing energy consumption under varying operating conditions. The design approach employed a Battery Management System (BMS) architecture enhanced with active thermal management, including temperature sensors, actuators, and adaptive control logic. The simulation was conducted under dynamic load conditions using a multi-module configuration to emulate real-world electronic systems such as electric vehicle battery packs. Key parameters evaluated included vehicle speed, battery pack temperature, pump power, and refrigerant power. Results showed that the system effectively reduced and maintained the battery pack temperature from approximately 30°C to 20°C, demonstrating efficient heat dissipation and temperature stability. The pump and refrigerant subsystems exhibited adaptive behavior, automatically adjusting power consumption in response to real-time temperature variations. This confirmed the system’s ability to achieve energy-efficient cooling and dynamic thermal control without manual intervention. The findings indicate that the proposed mechatronic cooling system successfully integrates sensors, actuators, and intelligent control algorithms to ensure effective and autonomous thermal regulation. The system’s energy optimization, realtime adaptability, and scalability make it suitable for diverse applications such as battery cooling, processor thermal management, and power electronics systems. In conclusion, the study validates the potential of mechatronic-based cooling systems as a reliable, efficient, and sustainable solution for modern electronic devices. Future work should focus on hardware prototyping, controller optimization using advanced algorithms, and exploration of alternative cooling fluids to further enhance system performance and sustainability.

Rapid urbanization, particularly in places like Nigeria, intensifies waste management challenges due to the inefficiency and high cost of traditional collection. While current smart waste monitoring and management systems offer improvements through IoT monitoring, they often lack advanced sorting and comprehensive management features. This project directly addresses these gaps by designing an innovative smart waste monitoring and management system that critically incorporates the automated segregation of metals and plastics, alongside enhanced automation and real-time data capabilities. Our prototype is a sophisticated solution that integrates various sensors—including ultrasonic and load sensors for fill-level monitoring and compaction, plus specialized inductive (for metals) and optical/capacitive (for plastics) detectors for sorting—with linear actuators for
automated processes and a GSM/GPS module for wireless communication. This setup allows the system to not only monitor bin levels, automatically compact waste, and control the lid, but most importantly, to accurately sort plastic and metal materials at the source. This ensures more efficient resource recovery and recycling. Through rigorous testing of both hardware and software, we will verify the reliability of
sensor performance, the effectiveness of automation, the accuracy of communication, and the precision of the waste segregation mechanism. The anticipated outcome is a fully integrated system that successfully demonstrates its capacity to accurately detect fill levels, initiate automated compaction, send prompt location-based alerts, and effectively sort waste. This proven dependability under real-world conditions highlights the system's potential to significantly alleviate urban waste management issues by reducing overflow, boosting collection efficiency, increasing recycling rates, and providing a scalable, sustainable solution for residential, commercial, and municipal settings. settings.

This report presents a comprehensive design and SolidWorks-based modeling of a conceptual container vessel equipped with an integrated robotic cargo-handling crane. The project combines core naval architecture principles with advanced parametric modeling tec0hniques to develop a structurally coherent, hydro dynamically efficient, and operationally automated vessel concept. The aim of the study is to demonstrate how modern CAD tools can be applied to marine engineering design while integrating automation systems that enhance vessel functionality and operational efficiency. The modeling process covers the systematic construction of the hull, deck arrangement, superstructure, bulwarks, and the robotic crane system using sketches, extrusions, lofts, reference planes, and surface features. Material properties such as mild steel, aluminum alloy, and anti-fouling coatings were applied to approximate real marine construction and enhance visualization accuracy. Design considerations including hydrodynamic efficiency, vessel stability, structural integrity, and safety guided all modeling decisions, particularly the placement and structural support of the onboard robotic crane. The inclusion of the robotic crane demonstrates the potential for automated cargo operations, reduced human involvement, and improved port efficiency. Rendering and surface finishing techniques further enhance presentation quality, making the model suitable for academic, industrial, and concept-evaluation purposes. Overall, the project showcases the practical application of CAD tools in modern marine engineering and highlights the relevance of integrating advanced automation technologies in contemporary vessel design.

This research presents the design and implementation of an integrated fuzzy logic–based decision-making system for autonomous vehicle navigation, focusing on intelligent speed regulation, steering control, and lane keeping. A three-degree-of-freedom (3-DoF) dual-track vehicle dynamics model was developed in MATLAB/Simulink to capture longitudinal, lateral, and yaw behaviors. The control architecture uses a Takagi–Sugeno fuzzy inference system to process speed error, distance error, and yaw deviation, generating throttle and steering actions that emulate human driving intuition. A simulation-based framework was developed for both ego and target vehicles, enabling the evaluation of inter-vehicle distance, trajectory following, and lane stability across straight and curved road sections. Results show that the fuzzy controller reduced longitudinal speed error to below 0.25 m/s, maintained lateral deviation within ±0.12 m on curved paths, and improved yaw rate tracking with a settling time of 1.8 s compared to 3.1 s without fuzzy control. The controller also limited throttle oscillations to less than 5% and sustained a safe inter-vehicle distance with less than 7% deviation from the desired headway. Overall, the research establishes a computationally efficient fuzzy-logic framework suitable for autonomous-vehicle applications, and the findings confirm the controller's robustness and adaptability in a virtual test environment.

The transportation and logistics sector could become much safer and more efficient with the use of autonomous vehicle (AV) technology. This project presents the design, development, and evaluation of a mini autonomous vehicle aimed at demonstrating the feasibility of low-cost, compact automation for indoor and controlled outdoor environments. The vehicle uses an ultrasonic sensor for real-time obstacle detection, with a microcontroller(ESP32)-based control system to execute navigation and collision avoidance maneuvers. A modular architecture was implemented, incorporating sensor data processing, path planning, and control algorithms to ensure responsive and adaptive vehicle behaviour. Both simulation and field tests were conducted to validate system performance, with results indicating reliable obstacle detection, effective trajectory tracking, and robust control response under various operating conditions. The successful realization of this mini autonomous vehicle project not only underscores the potential of accessible, cost-effective autonomous systems but also provides a solid foundation for future enhancements and applications in small-scale robotics.

An automated lawn mower is a machine designed to cut grass without requiring human guidance or control. With the continuous advancements in technology, automation has become integral to nearly every aspect of modern life. From household appliances to industrial machinery, automation has transformed the way we interact with our environments, reducing manual labour and improving overall efficiency. The emergence of automated lawn mowers follows this trend, replacing the conventional lawn mowing technology that demands significant human effort. This work aims to develop an improved automated lawn mower that
is both economically accessible and user-friendly, designed with locally sourced materials to minimize production costs. Unlike the existing robotic lawn mowers technologies, our model emphasizes a simple design making it easy to maintain and repair without specialized tools or skills. It is equipped with advanced sensor technology like the HC-SR04 ultrasonic and infrared sensors for obstacle detection and avoidance within the ranges of 10 to 50cm. When operating at a distance beyond 50cm, the mower consistently moved forward indicating an environment with no obstacles. Within a range of 30 to 50cm, the system effectively slowed the mower, achieving a 150millisecond response time and 98% accuracy. At closer proximities of 10 to 30cm, the mower reversed and turned with a slightly reduced accuracy of 95% and a 180millisecond response time, while obstacles detected at less than 10cm prompted at immediate stop within 120milliseconds at a 97% accuracy rate. It also integrates a 5kHz electromagnetic perimeter wire for systematic navigation and is powered by an 18V DC rechargeable battery, making it both sustainable and eco-friendly.

An Automated Residential Gate project aims to enhance security, convenience, and energy efficiency through the integration of automation and solar power technology. Traditional manual gates require significant human effort and are often inconvenient, especially for large or heavy gates. To address these issues, this project involves designing an automated sliding gate system controlled by remote access, keypads, and IOT connectivity. The system incorporates a D5V6 Smart Centurion Machine, a 60W solar panel, a 30A charge controller, and a deep-cycle battery to ensure uninterrupted operation, even during power
outages.The design includes a 0.37 kW motor with a gearbox to enhance torque efficiency, along with infrared sensors for obstacle detection and limit switches for precise movement control. Safety features such as emergency manual release and predictive maintenance alerts further improve usability and reliability. Structural materials such as steel and corrosion-resistant components ensure durability under various environmental conditions. Through performance testing, the system demonstrated smooth operation, energy efficiency, and enhanced security compared to conventional gates. The solar-powered system effectively reduces reliance on grid electricity, making it a cost-effective and sustainable solution. Future improvements may include AI-driven security enhancements and higher-efficiency solar panels to further optimize performance.