In this paper, advanced machine learning techniques called ensemble learning process (ELP) are proposed to predict the performance degradation of Li-ion batteries. The ELP is to use a variety of machine learning, Artificial Intelligence, and statistical modeling techniques and approaches to achieve the best results. Prediction quality and accuracy are quantified by the Pearson Correlation and Root-Mean-Square-Error (RMSE) respectively. A commercial software named ThingWorx Analytics (PTC) was applied to the study of predicting the capacity loss of Li-ion batteries using data from a dual dynamic stress accelerated degradation test (D2SADT). The results show that the proposed algorithms are able to precisely predicting the capacity loss of batteries in terms of a low root-mean-square error (RMSE = 0.01) and a high Pearson Correlation (0.99) for one step ahead prediction. Furthermore, the ensemble learning process confirms the good performance in the two steps ahead (long-term) prediction of degradation resulting with the only little increase of RMSE (from 0.01 to 0.02) and maintaining the same Pearson Correlation (0.99).