The paper introduces BattProDeep, a novel deep learningbased tool designed for the probabilistic prediction of battery aging. This tool stands out by addressing the limitations of traditional deterministic models and incorporating advanced probabilistic frameworks to accurately predict battery longevity under various conditions. It can be effectively implemented in real-life settings using a Battery Management System (BMS) that monitors the input parameters for this tool. For successful integration, the BMS must be capable of high-frequency data acquisition and have robust computational capabilities to handle the algorithms of BattProDeep. Enhancements such as improved data logging, real-time processing capabilities, vanced communication interfaces will enable the BMS to utilize BattProDeep for real-time battery health predictions under diverse conditions. Throughout our investigation, BattProDeep demonstrated its comparable improvements in battery aging predictions across multiple test scenarios. In static condition validations, the tool effectively captured the complexities of battery aging, including non-linear behaviors and rare anomalies. The dynamic validation further confirmed the model's efficacy in adapting to varying operational conditions, thereby affirming its comparable performance over traditional models like the Naumann model, especially under conditions with significant cyclic effects. Furthermore, the tool's capabilities were rigorously tested against hypothetical data in the new scenarios. While true experimental data was not available for these scenarios, the simulations provided by BattProDeep indicated distinct aging trajectories, highlighting its versatility and adaptability to different operational strategies. Looking ahead, several promising directions exist for enhancing the BattProDeep framework. Integrating physicsbased models with our current machine learning approach, as suggested by , could improve predictive accuracy by leveraging the strengths of both methodologies. For instance, incorporating conventional semi-empirical methods, such as the method proposed in , into the BattProDeep's training process can enhance the models' accuracy and address issues related to inadequate training datasets. Acquiring new test points, especially for shallow cycles, will refine the models further, enhancing their ability to adapt to varying battery usage patterns. A comparative study of different cycle analysis approaches is warranted since cycle detection methods can significantly influence predicted aging trajectories. Lastly, integrating physical battery aging equations into the data-driven training process presents a compelling improvement avenue. These enhancements will bolster the tool's robustness and extend its applicability across a broader spectrum of battery technologies and applications, contributing to the broader adoption and advancement of electric vehicle technology.