With the increase in the use of Lithium (Li)-ion batteries for Electric Vehicles (EV) applications, it is imperative to think about using it sustainably. The circular economy suggests to reuse or re-purpose the End of Life (EoL) EV batteries in less demanding applications. The State of Health (SoH) is an essential indicator in making decisions while reusing or repurposing EoL Li-ion batteries of EVs. Conventional SoH estimation often requires capacity measurement from the battery's full charge to cut-off state, which is quite challenging. In this paper, we propose an Incremental Capacity (IC) curve based SoH estimation system for Li-ion batteries. The model employs a Kalman filter and a finite differencing method for measurement noise attenuation. A novel method that combines Support vector regression (SVR) and the Autoregressive Integrated Moving Average (ARIMA) model is utilized to model the relationship between IC and the SoH. A use case is created on the NASA AMES open-source battery data. The case study shows that the proposed model can obtain accurate SoH prediction results without needing the State of Charge information of the battery