Electric Vehicles (EV) have been one of the most encouraging ways to tackle the adverse environmental effect of hydrocarbon-based transport. Most of the EV use Lithium(Li)-ion batteries as their power source, owing to their high energy density. In EV, due to the high-power energy requirements, the battery End of Life (EOL) is reached when the capacity degrades to an 80 percent of the original capacity. Therefore, there is still available capacity that can be re-purposed as a second life in less demanding applications. A reliable circular industrial system can be developed which should be able to transform post-used EV batteries into new added-value batteries to prolong their life and ensure more sustainability. Predicting the reliability of a system in its actual life cycle conditions and estimating its time to failure is helpful in decision making for the new value chain. This paper conceptualize the fusion approach to the prognosis for estimating Remaining Useful Life (RUL) of Li-ion batteries, incorporated with Failure Modes, Mechanisms, and Effects Analysis (FMMEA) to enhance prognostics planning and implementation. Hidden Markov model has been suggested as an online data-driven tool for detecting abnormal behavior and prediction of future health states.