Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Devarakonda, Seshachalam
Unknown Affiliation
Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Dimensionality reduced deep learning-based state of health estimation of Lithium-Ion batteries using standard dataset Srikantappa, Vimala Channapatna; Devarakonda, Seshachalam
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1942-1950

Abstract

Lithium-Ion batteries are used in everyday DC equipment’s, electric vehicle technology, and microgrid technology. The necessity to verify the battery's state is crucial for the dependent apps to continue operating without interruption due to advancements in battery technology & adaption. This study uses dimension decreases in input attributes along with deep learning methods to determine the state of health of lithium-Ion batteries (LIB). principal component analysis (PCA), a deep learning technique, is combined with recurrent neural networks (RNN) to reduce dimensionality. For the purpose of evaluating the effectiveness of the dimensionality reduction used in the data, the state of health (SOH) estimate using the RNN with and without PCA is compared. The use of PCA-powered RNNs using mean square error (MSE) as the loss function throughout the training and testing stages of state-of-health (SOH) estimation showed great performance in terms of loss. This was seen during the training and testing processes' respective testing and validation phases.
Lithium-ion battery charge-discharge cycle forecasting using LSTM neural networks Srikantappa, Vimala Channapatana; Devarakonda, Seshachalam
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2831-2840

Abstract

An important component for the dependable and safe utilization of lithium-ion batteries is the ability to accurately and efficiently predict their remaining useful life (RUL). In this research, a long short-term memory recurrent neural network (LSTM RNN) model is trained to learn from sequential data on discharge capacities across different cycles and voltages. The model is also designed to function as a cycle life predictor for battery cells that have been cycled under varying conditions. By leveraging experimental data from the NASA battery dataset, the model achieves a promising level of prediction accuracy on test sets consisting of approximately 200 samples.
Co-Authors Srikantappa, Vimala Channapatana Srikantappa, Vimala Channapatna