Design and Development of Battery Management System for Automated Car Using Machine Learning

The battery management system (BMS) is an essential component of electric and hybrid cars. The BMS's aim is to ensure safe and dependable battery operation. To keep up this, we need to monitor two parameter indicators, Charge Status and Health Status. SoC reflects the battery's current capacity expressed in terms of its rated capacity, and precise SoC is required in the control circuit to provide optimal charging process control. Battery life is very much affected by the temperature of the battery also Overvoltage and over charging affects the battery life drastically. Machine Learning is used to eliminate these problems and improve the battery life. Machine Learning uses battery parameters to estimate the charging pattern of the battery from the solar panel. In order to prevent complete draining of the battery and to provide uninterrupted power source to automated vehicle, two batteries are used on board. Also, the charging and discharging processes are autonomous in the system design, boosting system reliability. The batteries are powered from solar panel and regulated to give a constant supply. Dual battery selectors and a clever microprocessor are used to provide energy to the system as needed. While one battery charges from the solar panel, the other battery discharges. Machine learning is employed to control the charging of the battery by taking into account the temperature and voltage of the battery. This prevents complete draining of the battery and controlled charging pattern, thus increasing the life of the battery.