Battery Management System for Analysing Accurate Real Time Battery Condition using Machine Learning

The energy storage system is one of the essential components of Electric Vehicles (EV) that is anticipated to penetrate the current transport market because of the constant increase in environmental pollution and prices. Most EVs use lithium-ion batteries as their source of power. Lithium-ion batteries are rechargeable batteries that are typically used to power portable devices and EVs as well as Hybrid Electric Vehicles (HEVs) (powered both by fuel and electricity). The battery performance degrades progressively with time leading to some potential disasters. Current approaches for data-driven fault prediction provide good results on the exact processes they were trained on but here the batteries often lack the ability to adapt to flexibly change. To overcome the problem, here use Sequential learning which promises flexibility, allowing for an automatic adaption of previously learned knowledge to new tasks. Thus, it provides the State of Charge (SoC) of the battery and predicts its condition, which gives highly accurate results.