This research introduces tools for Materials Acceleration Platforms to enhance autonomous experimentation and predictive analysis tailored for battery technology. It integrates laboratory devices, multiple software, data analysis, and management systems and leverages machine learning and deep learning algorithms for AI-accelerated experimental optimization. The goal is to expedite knowledge extraction and increase the reliability and reproducibility of high-throughput experimental frameworks.