A Comprehensive Investigation of Fully Networked Neural Networks for Determining the Remaining Useful Life of Lithium Ion Batteries

This study aims to assess the performance of Fully Connected Networks, a kind of neural network model designed to predict batteries Remaining Useful Life. Each framework highlights that the model accuracy may be gradually improved by adding complexity or by applying different techniques like cross validation, regularization, dropout, early stopping and model ensemble. The Kaggle dataset provided the study dataset on lithium ion batteries. A dataset of about 1500 records were used in the analysis. Using the Colab platform, algorithms with various features were tested, and the outcomes were examined. After testing fully connected networks with various layers, regularization, dropout, early stopping, and numerous models, an ensemble technique demonstrated the best accuracy and lowest error values among fully connected networks with multiple models. The fully connected neural network using the ensemble approach performs comparably to the best single model in the comparison of fully connected neural networks under test cases algorithms for RUL prediction, although it may be more stable and less sensitive to data noise. The forecasts become more dependable when there is a small boost in performance and stability as a result of model averaging.