Adaptive SOC Estimation and Improvised Cell Balancing Techniques with IoT-Based Scalable Modular Li-ion Battery Management System

Electrical energy storage has seen significant advancements and is currently dominated using lithium-ion (Li-ion) cells. These lithium-based cells must operate under specific conditions to ensure system safety. Battery Management System (BMS) plays a crucial role in maintaining safe operation. The BMS proposed in this work keeps the battery pack functioning within safe limits across various parameters, while its modular design allows for scalability. This modular approach offers several benefits, including improved fault isolation, diagnostics, redundancy, ease of integration, and overall flexibility of the modules. In this study, simulations are conducted to explore State of Charge (SOC) estimation techniques, with the adaptive combined Fuzzy Logic (FL) and Coulomb Counting (QC) methods demonstrating minimal error in assessing the battery pack's status. A passive balancing technique is also developed for the modular Li-ion BMS using MATLAB/Simulink®. A real-time hardware prototype of the Modular Li-ion BMS is implemented with the Internet of Things (IoT) for continuous monitoring and control of the entire modular battery pack. The BMS includes essential safety features such as passive cell balancing, over-voltage, under-voltage, over-current, temperature monitoring, and SOC estimation, which are critical parameters for the safe operation of a lithium-ion battery pack.