DefinePK

Abstract

As the adoption of electric vehicles (EVs) continues to grow, the importance of efficient and reliable battery management systems (BMS) becomes increasingly critical. A Smart Battery Management Solution (SBMS) plays a pivotal role in optimizing battery performance, ensuring safety, extending battery life, and enhancing the overall driving experience. This paper explores the integration of advanced algorithms and real-time monitoring techniques in BMS to improve the performance and efficiency of EV batteries. The proposed solution leverages cutting-edge machine learning algorithms for accurate state-of-charge (SOC) estimation, predictive maintenance, and fault detection, while real-time monitoring enables dynamic adjustments to battery parameters, such as temperature, voltage, and current. Additionally, the paper examines the role of thermal management in mitigating risks such as overheating and thermal runaway, further enhancing battery safety and longevity. By combining real-time data analytics, predictive modeling, and intelligent control systems, this research proposes a comprehensive approach to battery management that not only maximizes the efficiency and lifespan of the battery but also contributes to the overall sustainability of electric vehicles. The results underscore the potential for these advanced techniques to revolutionize the way battery systems are managed in EVs, ultimately leading to safer, more efficient, and longer-lasting energy storage solutions.