Abstract
In recent years, rapid growth of renewable energy systems and electric vehicle technologies has intensified the focus on lithium-ion batteries as critical energy storage components. Consequently, evaluating their performance and lifespan has become a significant concern in both scientific research and industrial applications. The accurate remaining useful life (RUL) prediction of lithium-ion batteries are major importance to increase energy management efficacy and increase the battery lifetime. Recently, the quick advancement of machine learning (ML) and artificial intelligence (AI) knowledge, data-driven approaches for forecasting the lithium-ion battery duration are involved extensive attention. Numerous researchers are employing deep learning (DL) approaches to make several techniques for predicting RUL, namely recurrent neural network (RNN) and convolutional neural network (CNN). This paper introduces a temporal deep representation learning based remaining useful Lifecyle prediction (TRDL-RULP) approach for electric vehicle lithium-ion batteries. The main goal of the TRDL-RULP framework is to support intelligent battery management methods and contribute to increasing the operational safety and lifecycle management of electric vehicle energy reserves systems. Initially, raw battery degradation data are processed using data normalization to eliminate scale variations and improve model stability. To enhance feature relevance and reduce dimensionality, a snake optimization-based feature selection strategy is employed to enable the extraction of the most informative degradation indicators. For RUL prediction, a long short-term memory autoencoder has been exploited to capture nonlinear temporal dependencies and extract robust latent representations. Finally, the tuna swarm optimization algorithm can be applied for optimal optimize parameters to improve convergence speed and predictive performance. The simulation study of the proposed TRDL-RULP algorithm is conducted using a benchmark lithium-ion battery degradation dataset obtained from the Kaggle repository. Extensive comparative results demonstrate the superior performance of the TRDL-RULP approach over recent methodologies.