The Development of a Lithium-Ion Battery Analyzer for Data-Driven Analysis of Battery Performance

As demand for higher energy density, faster charging, and longer lifespan increases, so too does the need for sophisticated analysis of battery performance. This study focuses on the development of a lithium-ion battery analyzer for data-driven battery performance analysis. The system comprises a hardware platform and a cloud-connected software infrastructure. The hardware utilizes an STM32 microcontroller integrated with an LTC6803 for precise battery parameter measurements, including voltage, current, and temperature. The charging circuit is an LTC-4162, allowing for controlled charging profiles. A Raspberry Pi, running Raspbian OS, acts as the central processing unit, collecting data from the microcontroller and displaying it on a user-friendly human machine interface developed using Node-RED. Crucially, the system leverages cloud connectivity for data storage, analysis, and remote monitoring. The system transmits measured data to a cloud platform, which offers scalable storage and facilitates the development of sophisticated data analysis algorithms. This cloud integration allows for real-time battery performance monitoring and visualization through a dedicated application. Additionally, the system incorporates a local data backup mechanism, ensuring data accessibility even during internet disruptions. The developed system provides a comprehensive platform for data-driven insights into battery health, enabling informed decisions regarding battery operation and maintenance.