The increasing demand for green energy has made electric vehicles (EVs) a sustainable solution for modern transportation. Lithium-ion batteries are widely used in EVs due to their high energy and power density; however, they must operate within a safe operating area (SOA) to avoid safety risks. Hence, a Smart Battery Management System (BMS) is essential for ensuring safe, efficient, and reliable battery operation.
This project proposes a Smart BMS integrated with active cell balancing and battery life estimation using a Raspberry Pi 4. The system monitors key battery parameters such as voltage, current, and temperature through sensors. Machine Learning (ML) algorithms are employed to estimate the State of Charge (SOC) and State of Health (SOH), enabling accurate prediction of battery performance and lifespan.
Based on SOC and SOH values, the Raspberry Pi 4 controls relays to automatically manage the charging process, ensuring optimal battery usage and preventing overcharging or deep discharge. Active cell balancing is implemented to maintain uniform voltage levels across battery cells, thereby enhancing battery efficiency and lifespan.
The system provides real-time monitoring by displaying parameters such as voltage, current, temperature, SOC, and SOH on an LCD. A buzzer alert is triggered when parameters exceed safe limits, ensuring safety. Overall, the proposed Smart BMS improves battery performance, extends battery life, and enhances safety, making it suitable for advanced electric vehicle applications.
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