A precise prediction of the remaining useful life for energy storage lithium-ion batteries is critical to the safety and reliability of power systems. To solve the problem of serious nonlinear changes of the aging trajectory of lithium-ion batteries, this paper proposes an ensemble empirical mode decomposition (EEMD) and an ensemble machine learning-based RUL prediction method. First, the measured raw lithium-ion battery aging data are decomposed using the EEMD algorithm. Then, a long short-term memory (LSTM) neural network and the relevance vector machine (RVM) algorithm are integrated and applied to model and predict the residual sequence and the intrinsic mode sequences obtained by decomposition. Finally, the future lifespan aging trajectory of the lithium-ion battery is acquired by fusing the predicted residual sequence and the intrinsic mode sequences. The aging data of energy storage lithium-ion batteries are employed to validate the proposed method. The results show that the proposed RUL prediction method for lithium-ion batteries has better robustness and nonlinear tracking ability.