Deep Learning for Battery Thermal Optimization: LSTM, GRU, BiLSTM, and DNN in IoT-Driven Energy Systems

Abstract

Electric vehicle (EV) efficiency, reliability and safety are all greatly influenced by battery thermal management systems (BTMS). This study investigates the optimization of BTMS utilizing cutting-edge machine learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM) and Deep Neural Networks (DNN). Using a rich data set of batteries made up with ionized lithium metrics observed in diverse temperatures. This study constructs forecasting models to estimate the state of charge (SoC) and temperature in battery systems. Comparative analysis reveals that DNN excels in predictive accuracy for stable conditions, while BiLSTM offers superior performance in dynamic and high-temperature scenarios.

The work emphasizes challenges and merits combining aging effects into SoC and temperature predictions, providing a holistic approach to understanding long-term battery behaviour. By replacing costly physical prototyping with simulation-based evaluations, this research provides a transformative framework for rapid BTMS development. The proposed framework not only enhances the accuracy of thermal and charge state predictions but also provides a cost-effective simulation-based methodology for testing and optimization. The findings opens pave a path for innovative, reliable, as well as adaptable solutions for battery management that enhance adoption related to sustainable energy techniques. By helping to create more resilient, dependable and effective thermal management techniques for EV batteries, the current research prepares the foundation for future advances in battery optimization.