Publication

Abstract : As electrification advances in the automotive industry, attention towards the safety and reliability of the battery becomes important. The Battery Management System [BMS] which is the heart of the whole Battery system needs to be periodically inspected to ensure the safety, reliability, and longevity of EVs Li-ion batteries. Therefore, the study covers different hybrid machine learning (ML) approaches for monitoring remaining charge and EV battery health through hybrid machine learning (ML) such as LSTM-SVR, LSTM-ARIMA and LSTM-ATTENTION in two different platforms PYTHON IDLE and RASPBERRYPI 4B and ultimately suggest the best ML Hybrid Model to determine the state of charge and health of the battery.