Research, including a comprehensive review by , highlights the importance of understanding battery degradation mechanisms and integrating machine learning technologies to enhance prediction precision. Zhang et al. identify deterministic capacity estimation as a key challenge, emphasizing the need for robust predictive and probabilistic methods. This focus on recent advancements builds on foundational studies in the field. Classical battery aging models and early machine learning applications in this domain, such as , have laid the groundwork for current innovations, driving the shift towards more sophisticated and accurate predictive models. Further evidence of this trend is found in the works such as Challier et al. . These studies collectively highlight the effectiveness of integrating deep learning into battery degradation forecasting. The emergence of hybrid models, such as that proposed by , which utilize deep learning for analyzing relaxation voltage curves, further illustrates the capacity of these advanced computational techniques to revolutionize battery health assessment. This confluence of deep learning and probabilistic modeling marks a significant shift in the approach to battery degradation prediction, promising not only improved precision but the potential to redefine energy storage and management strategies for the future by providing an uncertain confidence interval. This focus on recent advancements is complemented by foundational studies in the field. Classical battery aging models and early applications of machine learning techniques have laid the groundwork for current innovations. For instance, the seminal work on battery health monitoring using open-source tools, such as , showcases the early integration of machine learning for battery aging predictions.