Neeraj Sanjay Mogal

Developed an embedded-ready fault detection and isolation system for DC microgrids as part of M.Sc. thesis research. Applied physics-guided machine learning to achieve real-time fault classification with high accuracy and low inference latency. Findings accepted for presentation at PCIM Europe 2026.

Built CAD models in Fusion 360, integrated into Unity and programmed in C# for VR engineering applications. Developed Raspberry Pi-controlled smart-home devices for STEM education.

Analysed lubricating-grease bleeding measurements using LabVIEW. Processed viscometer datasets with MATLAB for tribological characterisation.

Drove the end-to-end development of steer-by-wire systems by engineering and validating electromechanical actuators and digital twins (ANSYS, MATLAB/Simulink), complemented by targeted technology screening and detailed stakeholder reporting.

Engineering calculations, design drawings and cost estimates for jigs and fixtures using CREO and CATIA.

Power electronics, embedded systems, EV/HEV requirements engineering, electromobility, CAN bus simulation. Thesis: Fault detection and isolation in meshed LVDC microgrids using ML pipeline

Analog & digital electronics, microcontrollers, control systems, signal processing, MATLAB. CGPA: 8.09/10 — First Class with Honours.