Speed Control of BLDC motor using Fuzzy Logic Algorithm for Low Cost Electric Vehicle

Poornesh, Kavuri; Mahalakshmi, R.; Ram, Jayadeep Sai; N, Gunavardhan Reddy

doi:10.1109/icistsd55159.2022.10010397

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…It is important that the successful implementation of VANETs for collision prevention requires coordination among vehicle manufacturers, infrastructure providers, and government agencies [6]. Additionally, public awareness and acceptance of these technologies play a very important role for the prevention of vehicle collision.…”

Section: ) Traffic Management and Congestion Reductionmentioning

confidence: 99%

An Efficient and Intelligent System for Controlling the Speed of Vehicle using Fuzzy Logic and Deep Learning

Yadav,

Goyal

2024

IJACSA

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Vehicle collisions are a significant problem worldwide, causing injuries, fatalities, and property damage. There are several reasons for the collapse of vehicles such as rash driving, over speeding, less driving skills, increasing number of vehicles, drunk and drive, etc. However, over speeding is one of the critical factors out of all the reasons for vehicle collisions. To address the critical issues, the current article proposes a Fuzzybased algorithm to prevent and control the speed of the vehicle. The major objective of the proposed system is to control the speed of the vehicle for proactive collision avoidance. Deep learning and fuzzy system provide better integrated approach for the controlling of the speed and avoid vehicle collision. Fuzzification of the speed variable provides an advanced or viable solution for speed control. The current research used RNN and other deep learning algorithm to predict the traffic and identify the traffic frequency. The traffic frequency in a timeseries frame provides the frequency of the traffic within a time frame that can be detected by using involvement of IoT.

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Section: ) Traffic Management and Congestion Reductionmentioning

confidence: 99%

An Efficient and Intelligent System for Controlling the Speed of Vehicle using Fuzzy Logic and Deep Learning

Yadav,

Goyal

2024

IJACSA

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“…The literature is currently available and covers a wide range of control schemes. These include classical control laws [2,3], more advanced algorithms such as nonlinear control [4,5], fuzzy logic control [6][7][8][9], sliding mode control [10][11][12][13], backstepping control [14][15][16], and 𝐻∞ control [17,18]. Each of these strategies offers its own benefits and has been explored in various studies to optimize the performance of electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Linear and Nonlinear H-Infinity Control for an Electric Vehicle

Oudjama,

Boumediene,

Saidi

et al. 2023

JESA

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Permanent magnet synchronous motor-powered electric vehicles control is the subject of various research work. However, their global dynamic models are nonlinear and coupled. Therefore, to achieve efficient operation, an effective control system is crucial. In this study, we propose and compare linear H-Infinity and Galerkin approximation approach for Nonlinear H-Infinity control strategies to improve the durability and performance of post-driven speed in electric vehicles. In the case of linear systems, the linear H-Infinity controller is found by solving the algebraic equation known as the Riccati equation. On the other hand, the control problem based nonlinear H-Infinity poses challenges because it involves solving a nonlinear partial differential equation known as name of the Hamilton-Jacobi-Isaacs equation, which is difficult or even impossible to solve by using analytical methods. In these situations, the Galerkin approximation approach provides an approximation to the Hamilton-Jacobi equation solution. In order to evaluate the performance of Galerkin approximation approach and linear H-Infinity controllers, electric vehicle feedback simulations will be conducted, taking into account different constraints. The goal is to ensure efficient operation in different situations. The results demonstrate that the Galerkin approximation Approach for nonlinear H-Infinity controller reveals a similar performance and durability as the H-Infinity controller, and stands out for its ability to optimize the control system performance of the EV, providing a faster response, reducing undesirable ripples, and enhancing overall stability and precision. Generally, this comparative study brings to light the effectiveness of linear and Galerkin approximations for H-Infinity control in permanent magnet synchronous motor-powered electric vehicles. The results contribute to the advancement of control strategies and provide valuable information for the conception and employment of efficient electric vehicle control systems.

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“…Brushless DC (BLDC) motors are commonly used in electric vehicle drive trains to achieve maximum efficiency . BLDC motors are particularly significant in electric two-wheelers due to their high torque to weight ratio [28]. Sensorless control methods have been proposed as an alternative to rotor position encoders, but they often result in fluctuating rotor speed, known as the chattering phenomenon [29].…”

mentioning

confidence: 99%

Intelligent Controller Design for Electric Vehicle(EV) Speed Control Using An Inverse Model Based Reference Model Learning Algorithm

Bulut

2024

Preprint

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The fuzzy rules are knowledge base of a the fuzzy controller which are determined by using the system internal structure and experience information representing the behavior of the system. However, there is a need for automatic determination methods for the extraction of fuzzy rules, since the experience knowledge will be insufficient for systems with nonlinear or variable behavior. In areas where the control parameters vary, an intelligent system can be developed that uses the adaptive fuzzy method to find the coefficients of the controller in the system to overcome this. In this study, an adaptive fuzzy controller is designed using a learning-based fuzzy inverse model to provide speed control of the electric vehicle. The results obtained in the study showed that the designed inverse model learning-based fuzzy controller model is applicable for the dc motor that will provide speed control of electric vehicles. In the simulation of this model, system behavior has been investigated by way of use constant and variable loads. The obtained results remark that the proposed method satisfies system stability in terms of electric vehicle speed control.

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Speed Control of BLDC motor using Fuzzy Logic Algorithm for Low Cost Electric Vehicle

Cited by 12 publications

References 18 publications

An Efficient and Intelligent System for Controlling the Speed of Vehicle using Fuzzy Logic and Deep Learning

An Efficient and Intelligent System for Controlling the Speed of Vehicle using Fuzzy Logic and Deep Learning

Comparative Study of Linear and Nonlinear H-Infinity Control for an Electric Vehicle

Intelligent Controller Design for Electric Vehicle(EV) Speed Control Using An Inverse Model Based Reference Model Learning Algorithm

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