SMO Based Position Sensorless BLDC Motor Drive Employing Canonical Switching Cell Converter for Light Electric Vehicle

Saha, Biswajit; Singh, Bhim; Sen, Aryadip

doi:10.1109/tia.2023.3241607

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…This control technique is suitable only for light EVs. Regardless, the intermittent nature of solar power makes feeding power to EV motors a more difficult task [16]. Battery sources are mandatory with solar-based EVs, and a proper battery management system helps the SoC, the SoH, and the proper charging and discharging operations of the battery.…”

Section: Introductionmentioning

confidence: 99%

Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

Mathesh,

Saravanakumar,

Salgotra

2024

Energies

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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.

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Section: Introductionmentioning

confidence: 99%

Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

Mathesh,

Saravanakumar,

Salgotra

2024

Energies

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“…Ref. [29] discusses a sensor-less BLDC motor driver using a canonical cell converter. The INC MPP method-based PV-fed Zeta converter is elaborated in [30].…”

Section: Introductionmentioning

confidence: 99%

A Solar Photovoltaic Array and Grid Source-Fed Brushless DC Motor Drive for Water-Pumping Applications

Shukla,

Nikolovski

2023

Energies

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This article presents a brushless DC motor drive using a solar photovoltaic (PV) array and grid. Solar PV array-fed drive systems typically need a DC–DC converter stage in order to optimize the solar PV array-generated power utilizing a maximum power point (MPP) tracking technique. In this work, a boost DC–DC converter is used for MPP tracking purposes. This work utilizes an incremental conductance (INC) MPP-tracking algorithm. A bridgeless asymmetrical converter without a bridge rectifier is used at the grid side to improve power quality at supply mains. The presented asymmetrical converter is an amalgamation of a second order (buck boost) with a fourth-order (Cuk) converter, which lowers the net system’s order. The input inductor of the Cuk converter manages the input current profile and, thus, eradicates the need for the filter at the grid mains. The bridgeless asymmetrical converter comes with several advantages, such as rectifier removal, component reduction, and input filter elimination. The performance of the brushless DC motor is examined in this article in all three scenarios: first, when grid and solar energy are both present; second, when solar energy is the only source of energy; and third, when grid energy is the only source of energy. The dual-source-based brushless DC motor drive system has been developed on matrix-laboratory/Simulink. The results are deployed and discussed to verify the drive-system performance. The article also presents a detailed stability analysis and mathematical modeling of the presented power-quality converter and MPP tracking converter to verify different converters’ stability using a bode diagram and a pole-zero plot.

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Hybrid MGWO- CSO optimized high gain Relift Luo converter for PV based grid tied PMBLDC drive system

Prakash,

Boopathy

2024

Measurement: Sensors

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SMO Based Position Sensorless BLDC Motor Drive Employing Canonical Switching Cell Converter for Light Electric Vehicle

Cited by 9 publications

References 29 publications

Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

A Solar Photovoltaic Array and Grid Source-Fed Brushless DC Motor Drive for Water-Pumping Applications

Hybrid MGWO- CSO optimized high gain Relift Luo converter for PV based grid tied PMBLDC drive system

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