Temporal Deep Representation Learning Based Remaining Useful Lifecyle Prediction of Electric Vehicle Lithium-ion Batteries

The recent proliferation of renewable energy systems and electric vehicles has driven a growing need for accurate Remaining Useful Life (RUL) prediction of lithium-ion batteries, which are crucial for effective battery health management and operational safety. AI and deep learning algorithms have proven to be highly promising in data-driven battery degradation modeling and prediction, achieving remarkable accuracy in capturing the intricate dynamics of battery degradation. In this paper, we propose a Temporal Deep Representation Learning-based Remaining Useful Life Prediction (TRDL-RULP) framework for lithium-ion batteries in Electric Vehicles (EVs). The proposed framework proposes to improve the intelligence of the battery management in order to estimate the lifecycle with high accuracy and to support predictive maintenance strategies. Then, a Long Short-Term Memory Autoencoder (LSTM-AE) is used to extract nonlinear temporal degradation features and to learn robust latent representations for a RUL prediction. In addition, the Tuna Swarm Optimization (TSO) algorithm is used to optimize the network hyperparameters, which helps to enhance convergence efficiency and prediction accuracy. A benchmark dataset of lithium-ion battery degradation data from Kaggle is used for the model evaluation. The extensive experimental results show that the TRDL-RULP framework has better prediction accuracy, robustness and generalization ability than other machine learning and deep learning methods. This combination of deep temporal feature learning with intelligent feature selection and metaheuristic hyperparameter optimization offers an effective and scalable method for accurate battery health prognostics, which can effectively guide energy management and enhance the operational lifespan of electric vehicle battery systems.