Use of ML Techniques for Li-Ion Battery Remaining Useful Life Prediction-A Survey

Batteries made of lithium-ion material are crucially important for charge storage in Electric Vehicles. Most of the appliances use these batteries for the storage of energy which can be drawn as per the appliance requirement. It is important to know the reliability of the battery, as these batteries have a vital role in energy storage. As the number of cycles of usage of the battery increases there is always a change in the capacity of the battery even at 100 percentage State of Charge, once this capacity crosses the threshold of failure then it results in a dry cell and the cell does not hold the capacity to retain the charge. Therefore, Remaining Useful Life (RUL) becomes an important concept in Battery Management System (BMS) for industrial as well as academic research. The suitable method for RUL prediction along with the implementation of ML techniques are covered in this paper.