A novel feature adaptive meta-model for efficient remaining useful life prediction of lithium-ion batteries

Abstract

Lithium-Ion Batteries (LiBs) are the most widely used energy storage devices due to their high energy density and long cycle life. However, despite their widespread adoption, the stochastic nature of capacity degradation presents operational and safety challenges, diminishing the remaining useful life (RUL) of the batteries. This research introduces a multi-stage, feature-adaptive meta-model designed to optimize the latent vector space at the meta-data stage, enhancing subsequent meta-model learning. The adaptive nature of the meta-feature space minimizes prediction variance, thereby improving model generalization, prediction accuracy, and computational efficiency, achieving 51.34 % and 85.25 % greater accuracy compared to bagging and boosting methods, respectively. Furthermore, a bidirectional long short-term memory (BiLSTM) and variational autoencoder (VAE)-based generative model with an optimized latent dimension is developed to effectively capture statistical variations and temporal dependencies within the RUL dataset, addressing data availability challenges. Additionally, a cost-aware maintenance strategy is formulated, employing a quadratic function to assess the economic impact of precise RUL predictions by penalizing both overestimation and underestimation in different case studies. This study aims to deliver an accurate prediction model, a synthetic data generation method, and a cost-effective maintenance strategy for informed decision-making.