LSTM-Based Remaining Useful Life Prediction for Batteries: A Deep Learning Approach

Batteries are essential components of many products, including renewable energy storage systems and electric cars. Accurately estimating a battery's remaining useful life (RUL) is essential for minimizing maintenance costs, avoiding unexpected problems, and improving upkeep. Nonetheless, traditional prediction methods struggle greatly owing to the complex and often unpredictable patterns of battery degradation. This research proposes an LSTM-based approach for RUL prediction using time-series sensor data for better accuracy. With the benchmark battery dataset used for training and evaluation, the model surpasses benchmarks set by classical techniques. The results prove the proposed approach to be effective for real-world applications in battery prognostics, attaining higher R2 values while boasting lower MAE and MSE metrics.