Optimizing Battery Capacity Prediction Using Advanced Machine Learning Algorithms: A Comparative Analysis of LSTM, RNN, SVM, RF, and Kalman Filter

Abstract

Lithium-ion has important applications in portable devices, electric vehicles, and more recently, in large-scale energy storage systems. The prediction of battery capacity and remaining useful life or RUL is an important aspect which can be used in assuring reliability and safe operation of systems that employing batteries. In this research, the efficiency of different prospective algorithms: The technique involving the CALCE datasets for using LSTM, RNN, SVM, RF, and the Kalman Filter approach to predict the RUL of lithium-ion batteries is presented. The data will be further pre-processed depending upon the kind of look for non-linearity, as follows for all the models, their performance will hereby be checked with the help of the model validation on below said error: RMSE, MAE and Relative Error. The evaluation reveals that even though other models are good at enhancing the temporal dependencies of battery data, LSTM still performs better in terms of model accuracy and reliability of the temporal and non-linear relationships present in the battery data. The relevance of this research arises from the ability to establish suitable areas in applying machine learning strategies to IBMSs and enhance the performance and reliability of Li-ion batteries.