Intelligent Optimization of Battery Management System Parameters for Improved Performance of LiFePO₄ Batteries in Electric Vehicles

The growing demand for efficient, reliable, and safe electric vehicles (EVs) has elevated the significance of advanced BMS, particularly for LFP (LiFePO₄) Battery Technologies. This paper presents a comprehensive examination of major design criteria that influence the efficiencies, reliability, and longevity of Battery Management Systems (BMS) intended for electric vehicle (EV) use. Emphasis is placed on accurately estimating the State of Charge (SOC), an essential component of proper battery health monitoring and management of energy used.Various SOC calculation methods are analyzed, The study explores how these methods respond under different operational conditions and battery aging scenarios. Additionally, Key parameters including voltage balance, thermal strategies, current limits, and state-of-health (SOH) indicators have been assessed for their contributions towards system performance. The effectiveness of the proposed SOC estimation and parameter optimization has been validated through modeling and real-time testing. The insights gained from this work will aid researchers in the development of a durable and adaptable BMS design tailor-made for LiFePO₄ batteries used in today's electric vehicles. A potential divider-based current sensor monitors the load dynamics, while a temperature-stable OP-AMP filter design refines signal integrity. Experimental results validate system efficiency through 12 time- series plots (voltage, current, and power across PV modules), extracted from MATLAB and Excel-based post-processing. The solution is modular, programmable, cost-effective, and highly adaptable for smart-grid and off-grid solar applications.