Optimal light gradient boosting model-based prognostic approach for remaining useful battery life prediction

Predicting battery life in high-power applications is crucial for ensuring uninterrupted operations in fields like electric vehicles, aerospace, and renewable energy storage. Accurate life predictions enable proactive maintenance, reducing unexpected failures and downtime. In safety-critical sectors such as medicine and defence, timely battery replacement prevents malfunctions and ensures reliability. Effective battery management extends lifespan, minimises waste, and supports environmental sustainability through proper disposal and recycling. In smart energy systems, forecasting battery life enhances grid stability, load balancing, and renewable energy integration. This study employs a light gradient boosting model (LGBM) to predict the remaining useful battery life based on voltage and current behaviour. The model's performance is improved through optimised hyperparameters using grid and randomised searches. Addressing challenges like complex battery behaviour and variable conditions, the proposed approach is compared with state-of-the-art models, demonstrating competitive performance in remaining useful life (RUL) prediction.