EVS SRM Drive Control and Microgrid Integration

The switching reluctance motor (SRM) has garnered significant interest from both industry and academia due to its simple construction and reliance on readily available materials. However, controlling SRMs is more challenging than other types of motors, primarily due to issues like torque ripple, which is a key focus of research, especially for electric vehicles (EVs). SRMs offer cost-efficiency and high performance, with each operation phase being physically, magnetically, and electrically discrete. The absence of wires or magnets on the rotor also enhances its speed compared to other motors. Despite these advantages, SRMs require precise control and continuous monitoring of rotor position, which complicates their application. The motors are also known for generating more noise due to power ripple, which can be mitigated with proper control strategies. The primary goal of this thesis is to develop a highly efficient and durable SRM suitable for use in hybrid drive trains or purely electric vehicles. This research contributes firstly by designing an SRM drive that minimizes unnecessary estimations and maintains load stability. To accurately test and compare different control strategies, a realistic SRM model that avoids unwanted assumptions and accurately reflects machine behaviour under various load conditions is essential. The second contribution is the development of an improved adapter for EV SRMs, enhancing their operational efficiency. Both average and rapid torque control methods are necessary, with the optimal method selected for each operating point. A specialized converter has been designed, constructed, and tested on a bench to evaluate SRM performance in automotive applications. Additionally, the motor and generator operations are analysed for four-quadrant action to ensure smooth performance across a wide speed range. Simulation results demonstrate that the proposed models outperform existing approaches.

Authors

Institutions

Publication Details

Published
2024-10-04
DOI
https://doi.org/10.1109/gcat62922.2024.10923904
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EVS SRM Drive Control and Microgrid Integration

Alok Deshpande, Manoj Tarambale, Sunil Kumar
Parallel Computing and Optimization Techniques
article

EVS SRM Drive Control and Microgrid Integration

Alok Deshpande, Manoj Tarambale, Sunil Kumar
article en

Abstract

The switching reluctance motor (SRM) has garnered significant interest from both industry and academia due to its simple construction and reliance on readily available materials. However, controlling SRMs is more challenging than other types of motors, primarily due to issues like torque ripple, which is a key focus of research, especially for electric vehicles (EVs). SRMs offer cost-efficiency and high performance, with each operation phase being physically, magnetically, and electrically discrete. The absence of wires or magnets on the rotor also enhances its speed compared to other motors. Despite these advantages, SRMs require precise control and continuous monitoring of rotor position, which complicates their application. The motors are also known for generating more noise due to power ripple, which can be mitigated with proper control strategies. The primary goal of this thesis is to develop a highly efficient and durable SRM suitable for use in hybrid drive trains or purely electric vehicles. This research contributes firstly by designing an SRM drive that minimizes unnecessary estimations and maintains load stability. To accurately test and compare different control strategies, a realistic SRM model that avoids unwanted assumptions and accurately reflects machine behaviour under various load conditions is essential. The second contribution is the development of an improved adapter for EV SRMs, enhancing their operational efficiency. Both average and rapid torque control methods are necessary, with the optimal method selected for each operating point. A specialized converter has been designed, constructed, and tested on a bench to evaluate SRM performance in automotive applications. Additionally, the motor and generator operations are analysed for four-quadrant action to ensure smooth performance across a wide speed range. Simulation results demonstrate that the proposed models outperform existing approaches.

Kalinga University (IN)
Affordable and clean energy
Openalex Percentile: Top 68%
Parallel Computing and Optimization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.