Showing 14 results for Author: Praveen Kumar
Sep, 2026
Conference Paper
This paper presents a unified framework for comparing major electrical machine topologies under identical output and thermal constraints, with emphasis on supply-chain-aware selection among rare-earth-intensive, reduced-rare-earth, and rare-earth-free solutions. Using power factor and air-gap flux density as the principal descriptors, the framework links topology choice to…
Sep, 2026
Conference Paper
This paper presents a physics-aligned framework for fault diagnosis in multi-phase power-electronic systems using cycle-synchronous windowing and multi-domain features derived from Fourier, wavelet, and Hilbert–Huang representations. While both logistic regression and multilayer perceptron (MLP) models achieve perfect performance under standard evaluation, blind unseen tes…
Sep, 2026
Conference Paper
Knowledge-based artificial intelligence and hierarchical fuzzy logic offer an interpretable framework for electricvehicle motor preliminary design, but their computational burden grows with linguistic granularity and coupled design-space size. This paper presents a reduced quantum reformulation of the hierarchical fuzzy inference of air-gap flux density, a representative l…
Sep, 2026
Conference Paper
Grid-connected power electronic systems require timely and reliable fault detection to prevent equipment damage and reduce downtime. This paper presents a forecasting-based anomaly detection pipeline that decomposes voltage and current measurements into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), then trains a causal temporal convolutional net…
Mar, 2026
Conference Paper
The increasing demand for high-speed electric machines in many applications pushes the development of high-power density electric motors. Outer rotor motors, which can be designed with a larger airgap diameter than inner rotor motors for the same overall diameter and, therefore, provide higher torque, distinguish themselves in space-constrained but high torque applications…
Mar, 2026
Conference Paper
High-speed permanent magnet (PM) machines are widely used because of their high-power density and high efficiency. The high rotation speed also inevitably subjects the PMs to high centrifugal load, which might damage them due to their inherent mechanical vulnerability, such as a much lower tensile strength than the compressive strength. To robustly transfer the torque from…
Mar, 2026
Conference Paper
This study aims to investigate the propagation of initial cracks in electric motors and their rate of spread through the rotor structure under various operating conditions. Traction motors are subjected to high centrifugal load owing to their speed and rapid acceleration and deceleration. In the manufacturing process, some initial imperfections or cracks in the rotor compo…
Mar, 2026
Conference Paper
High–power density electric motor designs are a requirement in aerospace and automotive applications. Outer rotor permanent magnet motors can offer high power density but have mechanical challenges such as structural stability and rotodynamic issues. In this work, a rotodynamic study was performed for two outer rotor permanent magnet motor designs. The first design was a c…
Mar, 2026
Conference Paper
The rapid increase in electric vehicle (EV) adoption demands enhancements in the efficiency and adaptability of EV supply equipment (EVSE). Traditional EVSE systems often fail to optimize power delivery to meet the variable acceptance rates of EV batteries, resulting in significant energy wastage and reduced operational efficiency. This research addresses these challenges…
Mar, 2026
Conference Paper
Airports globally are shifting from ICE-powered to electric Ground Support Equipment (eGSE) to enhance efficiency, reduce operational costs, and improve operator health. Leveraging predictable routes, flat terrain, and low operational speeds, airports provide ideal conditions for electrification. This study evaluates freight GSE electrification at Dallas-Fort Worth Interna…
Mar, 2026
Conference Paper
This work presents a novel approach to optimizing electric vehicle motor design through the integration of Knowledge-Based Artificial Intelligence (KB-AI) and Hierarchical Fuzzy Logic. Traditional motor design processes are time-intensive, relying heavily on iterative simulations and domain-specific expertise. These processes are further complicated by the nonlinear relati…
Sep, 2025
Journal
Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integ…