Fuzzy Adaptive PI Controller for DTFC in Electric Vehicle

khessam, Medjdoub; Hazzab, Abdeldjebar; Bouchiba, Bousmaha; Bendjima, Meriem

doi:10.11591/ijpeds.v4i4.6372

Cited by 6 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1, February 2019: 320-327 322 light duration [21,22]. Other researcher uses the Fuzzy Mamdani method to design automated parking systems using fuzzy controls [23] and to control electric vehicle speed and torque, developing an adaptive cruise control to adjust the vehicle speed [24,25].…”

Section: Methodsmentioning

confidence: 99%

Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

Hartanti¹,

Aziza²,

Siswipraptini³

2019

TELKOMNIKA

View full text Add to dashboard Cite

One of the main causes of traffic congestion, especially at intersections, is because traffic lights have not been able to show the right time according to the existing traffic conditions. Time settings based on peak/off-peak traffic lights are not enough to handle unexpected situations. The fuzzy mamdani method makes decisions with several stages, the criteria used are the number of vehicles, the length of the queue and the width of the road to be able to optimize the time settings based on the real-time conditions required so that unwanted green signals (when there is no queue) can be avoided. The purpose of this research is to create a simulator to optimize traffic time management, so that the timers on each track have the intelligence to predict the right time, so that congestion at the intersection can be reduced by adding up to 15 seconds of green light from the previous time in the path of many vehicles.

show abstract

Section: Methodsmentioning

confidence: 99%

Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

Hartanti¹,

Aziza²,

Siswipraptini³

2019

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…In this section, fuzzy adaptive proportional-integral (PI) vontroller for (DTFC) is used as a reference and applied on an EV [8]. EV velocity is equal to 60 Km/h.…”

Section: Straight Route Casementioning

confidence: 99%

“…As a result, the final control data may be systematically analyzed using the proper Lyapunov's functions. A robust non-linear adaptive controller has been extracted directly from this PMSM speed control approach [8]- [15]. The controller is resilient against stator resistance, viscous friction instability and unpredictable load torque disturbance.…”

Section: Introductionmentioning

confidence: 99%

A new application for fast prediction and protection of electrical drive wheel speed using machine learning methodology

Khessam¹,

Lousdad

Hazzab³

et al. 2022

IJEECS

Self Cite

View full text Add to dashboard Cite

This paper introduces <span>a non-linear implementation of the speed control technique of permanent magnetic synchronous motors (PMSM) using electronic differential (ED) command. Artificial neural network (ANN) coupled with particles swarm optimization (ANN-PSO) are implemented to control wheel speed and steering angle. The main purpose of the PMSM system and its application is the command of electric vehicles (EV). In the controller design, three-phase currents and rotor speed shall be measurable and eligible for feedback. Our propulsion platform consists of two PMSM in the back. The study with implemented ANN-PSO is performed after collecting the data from the ED to manage the control of speed EV, Left and right of steering angle and steering ahead. Based on this strategy, a new application can be provided in the GPS application to give the information as input (curved path angle) to ANN-PSO. Next, the application of ANN-PSO can estimate the parameters of ED to avoid the slip, as well as improves better performance and dynamic stability of electric vehicle drive systems.</span>

show abstract

“…The road load 𝐹 𝑟𝑒𝑠 the formula is based on the principles of vehicle dynamics and aerodynamics [5]- [10].…”

Section: Analysis Of Vehicle Dynamicsmentioning

confidence: 99%

An application for nonlinear control by input-output linearization technique for pm synchronous motor drive for electric vehicles

Abdellah¹,

Hazzab²,

Khessam³

2022

IJPEDS

View full text Add to dashboard Cite

This paper leads to present the modified approach of the speed control for permanent magnet synchronous motors applied to electric vehicles using a nonlinear control. The motor's nonlinear dynamics are transformed into a linearized system model using the input-output feedback linearization technique. There are two permanent magnet synchronous motors (PMSM) in the propulsion model. In order to improve the motor's output torque, the direct component of the current is adjusted to zero. The electronic differential, which is used in the calculations, enables each driving wheel to be controlled individually at each curve. The MATLAB/Simulink software is used to implement modeling and simulation in order to assess the effectiveness of the suggested solution. Simulation studies are used to confirm the efficacy of the proposed technique. The obtained results signify that this approach is more accurate.

show abstract

Fuzzy Adaptive PI Controller for DTFC in Electric Vehicle

Cited by 6 publications

References 0 publications

Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

A new application for fast prediction and protection of electrical drive wheel speed using machine learning methodology

An application for nonlinear control by input-output linearization technique for pm synchronous motor drive for electric vehicles

Contact Info

Product

Resources

About