• Data-Driven Model Innovation: Our tool introduces a purely data-driven model that harnesses the capabilities of TensorFlow and TensorFlow Probability libraries, reflecting the latest advancements in machine learning and probabilistic modeling. Unlike traditional methods that may rely on specific battery chemistry knowledge or complex physical models, our data-driven approach allows for the adaptation and learning from real-world usage patterns and degradation behaviors. This method ensures that our predictions are grounded in actual performance data, enhancing the accuracy and relevance of the outcomes.