Lithium-ion batteries are widely used as power sources, but they degrade over time. Knowing the remaining useful life of Li-ion batteries, especially at the early stage of their lifespans, promotes the early detection of abnormal or faulty cells. However, there don’t currently exist commercially available software solutions that can streamline the process of data acquisition, data processing, and visualization, which are essential for future machine learning endeavors. By focusing on improving data acquisition and visualization capabilities, the aim is to lay a solid foundation for potential future machine learning applications.