Intelligent Predictive Battery Management System with Optimized Bayesian LSTM Network for Lithium-Ion Batteries

This paper presents an Intelligent Predictive Battery Management System (IPBMS) designed to enhance the longevity, safety, and performance of lithium-ion batteries in e-bikes. Conventional battery management systems often estimate State of Charge (SoC), State of Health (SoH), or Remaining Useful Life (RUL) independently, limiting their effectiveness in predictive maintenance. To bridge this gap, the proposed IPBMS provides a comprehensive framework that simultaneously estimates SoC, SoH, and RUL, enabling a holistic battery health assessment. Hot Deck Imputation addresses missing and inconsistent data to ensure data reliability, while an Unscented Kalman Filter models battery nonlinearities, enhancing SoC estimation accuracy. Additionally, an Optimized Bayesian LSTM, refined with Mountain Gazelle Optimization, captures temporal dependencies to improve SoH and RUL predictions, further refined using a dense regression layer that minimizes errors. The model achieves Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) of 0.59, 1.28, and 1.13 for SoC; 0.87, 1.50, and 1.22 for SoH; and 1.60, 1.28, and 2.69 for RUL, outperforming conventional models. Beyond prediction, the IPBMS offers actionable insights for charging optimization, early fault detection, energy-saving strategies, and adaptive riding modes, ensuring safer and more efficient battery utilization. This framework extends battery lifespan, minimizes unnecessary replacements, and enhances sustainability by optimizing energy management and reducing environmental impact, providing a novel, practical solution to improve battery reliability and efficiency in modern electric transportation.