PV-based Grid Interactive PMBLDC Electric Vehicle with High Gain Interleaved DC-DC SEPIC Converter

Kavin, K. S.; Karuvelam, P. Subha

doi:10.1080/03772063.2021.1958070

Cited by 53 publications

(15 citation statements)

References 31 publications

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“…The introduced method was utilized the shunt active power filter. Kavin and Subha Karuvelam [26] have elucidated the SEPIC converter for solar panel and grid utilizes the electrical vehicle system. The introduced converter was operate at low duty cycle and provides high dc voltage, less switching loss.…”

Section: Recent Research Work: a Briefly Appraisalmentioning

confidence: 99%

Active power management of grid-connected PV-PEV using a Hybrid GRFO-ITSA technique

Reddy¹,

Prasanth²,

Chandra³

2023

STET

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In this manuscript proposed a hybrid Garra Rufa Fish Optimization (GRFO) and Improved Tunicate Swarm Algorithm (ITSA) for improving the power quality of the integrated Photovoltaic (PV) and Plug-in Electric Vehicle (PEV) in Smart Grid (SG) system. The GRFO-ITSA approach is hybrid wrapper of GRFO and ITSA. Commonly it is named as GRFO-ITSA approach. The grid-connected PV-PEV, active power management is performed by the proposed approach. The proposed GRFO approach is used to determine the individual harmonic components and to reduce the recompense currents applied to PVs via PEV converters. The load flow control is performed by ITSA approach, which controls the power among the PVs, and PEVs. Additionally, it satisfies the power demand, and voltage variation. The proposed approach is also to analyze the mutual properties of PVs as well as PEVs on the feeder and transmitting loads, voltage outlines, harmonic alterations of an urban electric power distribution system. Also, the performance of the GRFO-ITSA is implemented on MATLAB site as well as associated with several existing approaches. The GRFO-ITSA have improved the power quality and compensate the harmonics and reactive power of the system. The optimal outcome is obtained by GRFO-ITSA with less computation time.

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Section: Recent Research Work: a Briefly Appraisalmentioning

confidence: 99%

Active power management of grid-connected PV-PEV using a Hybrid GRFO-ITSA technique

Reddy¹,

Prasanth²,

Chandra³

2023

STET

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“…One way to provide electric vehicles with an endless power supply is by using renewable energies. Renewables have been widely researched to find the optimum power to gain from them [12], [13], the engineering methods to extract power from them [14], [15], and the regulating rules to spread their use [16], [17]. Brake by wire system is introduced to enhance the implementation of self-driving cars.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of a new brake-by-wire system using machine learning

Hassanein

Dawod

Hassan

2023

IJECE

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One of the main aims of the recent research on brake-by-wire systems is to decrease mechanical components. In this paper, we propose replacing the brake pedal with a driving wheel that is fully covered by pressure braking batch sensors. The new mechanism for braking translates pressure exerted through the driver’s hands on the driving wheel to a corresponding electrical signal. A proposed design for the pressure braking batch (PBB) is made out of a mesh of conducting threads separated by a resistive sheet. To the best of our knowledge, this idea has not been raised before in other research papers. Different people have different muscle strengths and so the problem of identifying the intention of the user when pressing the PBB is tackled. For this aim, a new dataset of its kind is created by several volunteers. From each volunteer, age, gender, body mass index (BMI), and maximum pressure exerted on the driving wheel are collected. Using Weka software, the detection accuracy is calculated for a new volunteer to know the intention of his/her pressure on PBB. Among the three algorithms tried, the regression tree gives the best results in predicting the class of the pressure exerted by the volunteers.

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“…Generally, motor DC speed can be controlled by changing the voltage input of the motor. To change the magnitude of DC voltage, DC to DC converter is essential [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Controller of Bidirectional DC-DC Converter Based on Extreme Learning Machine for Electric Vehicle

2022

IJIES

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This paper proposed a novel adaptive controller of a non-isolated bidirectional coupled inductor DC converter based on an extreme learning machine (ELM) for electric vehicles. MATLAB and SIMULINK are used as the software platform to simulate the proposed method. Different operating conditions are considered to show the efficacy of the extreme learning machine. From the simulation results, it is found that by designing the controller based on an extreme learning machine the controller can automatically adjust the value depending on the operating conditions (motor moving forward, backward, and regenerative braking). In addition, the performance of the motor with DC converter based on ELM is compared with DC converter based on grey wolf optimization and based on conventional PI controller. This is indicated by the overshoot and the settling time is much better when using controller based on an extreme learning machine (overshoot 221.6 and settling time 3.8 second).

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PV-based Grid Interactive PMBLDC Electric Vehicle with High Gain Interleaved DC-DC SEPIC Converter

Cited by 53 publications

References 31 publications

Active power management of grid-connected PV-PEV using a Hybrid GRFO-ITSA technique

Active power management of grid-connected PV-PEV using a Hybrid GRFO-ITSA technique

Design and analysis of a new brake-by-wire system using machine learning

Adaptive Controller of Bidirectional DC-DC Converter Based on Extreme Learning Machine for Electric Vehicle

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