An insightful study by explores the potential of deep learning approaches, specifically leveraging Long Short-Term Memory networks (LSTM), for estimating the state of health and degradation of lithium-ion batteries for electric vehicles. This underscores the broader trend towards employing sophisticated machine learning techniques to navigate the complexities of battery health prediction without being dependent on an intricate understanding of the underlying degradation mechanisms (i.e., being physicsagnostic). This research aligns with the broader goal of creating an open-source tool for probabilistic battery aging prediction, utilizing deep learning techniques based on open-source datasets provided by Naumann et al. in their foundational studies. These datasets serve as benchmarks for future development in the field, underscoring the importance of accessible, high-quality data for advancing battery technology research. The models developed by Naumann, as implemented in and compared in , showcases the feasibility and effectiveness of machine learning models in predicting battery aging, setting a precedent for future work in this area.