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

Inhalt:
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 predictions. Traditional deterministic models for estimating battery longevity are inadequate, as they do not fully capture 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 collaboration across the global research community. This not only enriches the tool with a diverse range of insights but also 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 intervals, 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 benchmarking 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.