Adaptable battery charging and management system with chemistry detection and IoT integration

This paper introduces the design and implementation of a microcontroller-based battery charging and management system capable of identifying the cell chemistry and applying appropriate charging parameters. This is made possible by developing an algorithm that can identify cell chemistry by analyzing the discharge curve and classifying the cell accordingly. The paper further explains the process of categorization. The algorithm implemented differentiates between conventional lithium-ion (NMC, NCA, LCO), LFP, and NiMH battery chemistries. It was experimentally validated to be accurate without any prior identification provided by the user. The deployed system is also capable of implementing general battery management features, such as over-current protection, under-voltage protection while discharging, and over-voltage protection during charging. An IoT platform was designed to allow users to set the operational parameters and monitor, display, and analyze the results. The results show the cell chemistry determination to be accurate. The proposed system is low-cost and scalable due to its reliance on microcontrollers and algorithms.