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Prediction of the residual useful life of Lithium-ion batteries is one of the hotspots presently. In order to further obtain the residual useful life prediction of Li-ion battery, the degeneration data of it obtained from university of Maryland are analyzed. Discrete data point filtering is performed on the degraded data to simplify the processing. Due to the defects of slow learning speed and easy to fall into local minimum of the Back Proragation Neural Network (BPNN), the fast speed of Levemberg Marquardt (LM) algorithm and the globally search advantage of Genetic Algorithm (GA) are used to deal with. The construction of GA-LM-BPNN is three layers and is used to predict the residual capacity of Li-ion batteries. [ABSTRACT FROM AUTHOR]