Accurate state of health (SOH) estimation of rechargeable batteries is important for the safe and reliable operation of electric vehicles (EVs), smart phones, and other battery operated systems. We propose a novel method for accurate SOH estimation which does not necessarily need full charging data. Using only partial charging data during normal usage, 10 derived voltage values ([Formula: see text]) are collected. The initial [Formula: see text] point is fixed and then for every 1.5% increase in the Coulomb counting, other points are selected. The difference between the [Formula: see text] values ([Formula: see text]) and the average temperature during the charging form the feature vector at different SOH levels. The training data set is prepared by extrapolating the charging voltage curves for the complete SOH range using initial 400 cycles of data. The trained artificial neural network (ANN) based on the feature vector and SOH values can be used in any battery management system (BMS) with a time complexity of only [Formula: see text]. Less than 1% mean absolute error (MAE) for the test cases has been achieved. The proposed method has a moderate training data requirement and does not need any knowledge of previous SOH, state of charge (SOC) vs. OCV relationship, and absolute SOC value.