Dimensionality reduced deep learning-based state of health estimation of Lithium-Ion batteries using standard dataset

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.