Neural Network-Based Sensorless Control of Flyback Converter for Cell Balancing

Simple item page
dc.contributor.authorPremarajan, P.
dc.contributor.authorRaushan, R.
dc.contributor.authorBhushan, R.
dc.date.accessioned2026-02-06T06:33:35Z
dc.date.issued2025
dc.description.abstractA conventional control system with an active clamp flyback converter uses a feedforward method that involves feeding the input to a mathematical model beforehand to adjust the duty cycle accordingly. The feedforward method uses a mathematical model that involves a lot of calculations to obtain an efficiency alike feedback system. Integrating a neural network trained with a feedback system data output can be used as a replacement for the mathematical model to have performance as par with the feedback system. Sensors are used to measure the voltages for the feedback circuit to work. This work investigates the potential of utilizing a neural network to enable sensorless operation within a feedforward control system to regulate the voltage input to the cell balancina system. © 2025 IEEE.
dc.identifier.citation2025 IEEE 1st International Conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025, 2025, Vol., , p. -
dc.identifier.urihttps://doi.org/10.1109/SSDEE64538.2025.10967803
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/28710
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectCell balancing
dc.subjectConverters
dc.subjectFlyback
dc.subjectNeural network
dc.subjectSensorless control
dc.titleNeural Network-Based Sensorless Control of Flyback Converter for Cell Balancing

Files

Collections

Conference Papers