Published Paper Details:

The growing use of lithium-ion batteries in electric vehicles, renewable energy systems, and portable electronics has increased the need for efficient Battery Management Systems (BMS). Lithium-ion batteries are highly sensitive to voltage, current, and temperature variations, which can affect performance, safety, and lifespan. Hence, accurate monitoring and estimation of battery parameters are essential for safe and reliable operation. This project presents the design and development of a real-time Battery Management System for lithium-ion batteries with State of Charge (SOC) and State of Health (SOH) estimation using the Extended Kalman Filter (EKF). The system continuously monitors battery voltage, current, and temperature using a voltage sensor, INA219 current sensor, and Dallas DS18B20 temperature sensor interfaced with an Arduino Uno microcontroller. The EKF algorithm is implemented to estimate SOC accurately under dynamic conditions. Compared to conventional methods such as Coulomb counting and voltage-based estimation, EKF reduces errors caused by sensor noise, drift, and nonlinear battery behavior. SOH is determined based on battery capacity degradation, which helps analyze battery aging and health condition. To ensure battery safety, MOSFETs are used to control charging and discharging operations. The system automatically disconnects the battery during abnormal conditions such as overvoltage, undervoltage. A DC motor is used as a load to simulate discharge conditions. The measured and estimated parameters are stored in an SQLite database and displayed through a Flask-based web dashboard for real-time monitoring and visualization. Experimental results show that the developed system provides stable SOC and SOH estimation, reliable monitoring, and improved battery safety. The proposed system is cost- effective, scalable, and suitable for battery management applications in electric vehicles and renewable energy storage systems