Automated Battery Management System (BMS) for Electric Vehicles (EVs)

The growing demand for electric vehicles (EVs) has highlighted the need for advanced battery systems that are safe, efficient, and reliable. This project focuses on the development of an Automated Battery Management System (BMS) designed to intelligently manage and optimize battery performance in EVs. The goal is to extend battery life, enhance safety, and improve overall energy efficiency while supporting future technologies such as autonomous driving and vehicle-to-grid (V2G) communication.

The system design begins with defining performance and safety goals, followed by selecting appropriate hardware such as sensors and controllers. The project incorporates advanced algorithms to estimate the battery's state of charge (SOC) and state of health (SOH), balance cell voltages, and control temperature to prevent overheating. Machine learning and Kalman filter techniques are used for predictive maintenance and real-time monitoring.

Energy optimization strategies include adaptive charging and efficient power distribution across the vehicle's systems. In addition, the system is designed to support V2G technology, allowing the EV to contribute electricity back to the grid during peak demand.

To ensure reliability and user safety, the BMS includes automated protection mechanisms and intelligent fault detection that can notify users of potential issues. By integrating smart automation and predictive control, this project aims to make EVs more dependable and sustainable for widespread adoption.