BattProDeep: A Deep Learning-Based Tool for Probabilistic Battery Aging Prediction

Profitability, reliability, and efficiency of battery systems across a broad spectrum of applications, including both stationary energy storage and automobile sectors, are critically dependent on accurate battery lifespan predic-tions. Traditional deterministic models for estimating battery longevity are inadequate, as they do not fully cap-ture the complex and stochastic nature of battery degradation. In this contribution BattProDeep is introduced as a groundbreaking tool that employs a deep learning-based framework to offer probabilistic predictions of battery aging, thereby addressing the uncertainties according to the experimental dataset. BattProDeep sets itself apart with its innovative features. It adopts an open-source approach, enhancing transparency and fostering collabora-tion across the global research community. This not only enriches the tool with a diverse range of insights but al-so accelerates advancements in the field. Utilizing cutting-edge TensorFlow and TensorFlow probability libraries, BattProDeep offers a data-driven method for battery aging prediction, improving accuracy and applicability across different battery types and conditions. Furthermore, its probabilistic predictions include confidence inter-vals, providing crucial information about prediction uncertainty, which is invaluable for risk management and decision-making in critical sectors. The validation results show that the mean prediction error for our approach stays within ±0.2 % for high-cyclic applications, with all true measured capacity loss values falling within the 95 % confidence interval, affirming its reliability for risk management. These qualities, coupled with the bench-marking of BattProDeep according to the literature, make BattProDeep a key instrument for advancing battery health management, leading to more dependable and sustainable battery-powered solutions.