9th International Conference on ICT for Sustainable Development

Authors - Suhas M, B Sahithi Reddy, Ashwini N, Medini S Kalkur, Jyothi TN, Venugopal N
Abstract - Electric vehicles (EVs) have emerged as a promising solution to address environmental concerns and reduce reliance on fossil fuels. However, the performance and longevity of EVs are heavily dependent on the effectiveness of their battery management systems (BMS). A smart BMS plays a crucial role in optimizing battery performance, ensuring safety, and extending battery life. This paper delves into the design and implementation of a smart BMS for EV applications. The proposed BMS incorporates advanced algorithms and techniques to accurately monitor and control battery parameters. The system utilizes real-time data acquisition and processing to optimize charging and discharging strategies, ensuring efficient energy utilization and extending battery lifespan. The proposed method employs the Unscented Kalman Filter(UKF) algorithm to estimate State of Charge (SOC) based on the battery’s real-time voltage ,current, and temperature measurements . The Voltage, Current and Temperature parameters are utilized to capture the nonlinear behavior of the battery, accounting for the variations in battery performance under different operating conditions. A simulation study is conducted, and its results demonstrate the effectiveness of the UKF-based SOC estimation method. The same approach is used in the hardware implementation. This paper provides insights into high-frequency transformer design, potentially useful for Arduino system power supply construction, and suggests future research on battery management system integration for improved efficiency and accuracy.