Mathesh Gurusamy

Selective Harmonic Elimination (SHE) is a crucial modulation technique for minimizing total harmonic distortion in multilevel inverters. This study introduces a novel hybrid optimization approach, the Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) and Hybrid Grey Wolf Optimizer-Particle Swarm Optimization (HGWOPSO), to enhance SHE performance in both conventional Cascaded H-Bridge Nine-Level Inverters and a newly proposed Modified Reduced Switch Nine-Level Multilevel topology. The key innovation lies in the hybridization of Genetic Algorithm (GA) Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO), combining the explorative strength of GWO with the exploitative efficiency of PSO to achieve superior harmonic suppression. Additionally, the proposed Modified Reduced Switch Nine-Level Multilevel Inverter topology significantly reduces switch count while maintaining high power quality, offering improved cost-effectiveness and efficiency. Simulation and hardware results demonstrate that HGWOPSO outperforms standalone algorithms (GA, GWO, PSO) and other hybrid methods (GA-PSO, GWO-PSO), achieving the lowest Total Harmonic Distortion (THD) (6.08% phase voltage, 9.63% line voltage). The Modified Reduced Switch Nine-Level Multilevel Inverter further validates its practicality by reducing component usage without compromising performance. These findings highlight the superiority of hybrid optimization techniques in advancing multilevel inverter systems, particularly for applications demanding high power quality and energy efficiency.

Using multiple input power sources increases the reliability of electric vehicles compared to a single source. However, the inclusion of other sources exhibits complexity in the controller system, such as computing time, program difficulty, and switching speed to connect or disconnect the input power to load. To ensure optimal performance and avoid overloading issues, the EV system needs sophisticated control. This work introduces a machine-learning-based controller using an artificial neural network to solve these problems. This paper describes the detailed power management control methodology using multiple sources like solar PV, fuel cells, and batteries. Novel control with an instantaneous reference current scheme is used to manage the input power sources to satisfy the power demand of electric vehicles. The proposed work executes the power split-up operation with standard and actual drive cycles and maximum power point tracking for PV panels using MATLAB Simulink. Finally, power management with a machine learning technique is implemented in an experimental analysis with the LabVIEW software, and an FPGA controller is used to control a 48 V, 1 kW permanent-magnet synchronous machine.

Nowadays, more than one power source is needed to fulfil the power demand for electric vehicles. The multiple sources enhance reliability regarding the cruising range and decrease the charging cost. However, the inclusion of multi-sources generally gives rise to issues in the controller unit, such as slow response due to the immediate changes in load and power conversion complexity while switching the sources. This paper presents a novel intelligent control scheme based on fuzzy logic to mitigate this issue. The proposed controller includes a solar panel, a fuel cell, and a battery as input source. In this work, to examine the instantaneous reference currents from the sources and to manage power for the electric vehicle motor, a permanent magnet synchronous machine (PMSM) is considered. The proposed controller performs real-time power management, Maximum power point tracking (MPPT) for the PV system, and load calculations based on vehicle dynamics. The proposed work explores power management techniques for efficient control by utilizing real-time irradiance data from the Solcast website and drive cycle data collected at a university campus. Finally, the proposed controller was developed using MATLAB/Simulink and implemented as a hardware investigation with the LabVIEW tool and FPGA controller for a 1 kW PMSM to validate that the controller enables consistent power split operation in different load conditions.