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Aiming at the maintenance of Li-ion battery for life-supporting medical electronic equipment, a set of Prognostics and Health Management (PHM) was designed and the implementation framework of the PHM system was proposed. A data-acquisition process was conducted by constructing a battery-controlled stress level test platform and injecting faults into a lithium ion battery. A lithium-ion battery model based on the Arrhenius model of a medical electronic device was established. The probability of real-time failures was calculated by using the UPF and PF algorithms, and the remaining life prediction and health management maintenance methods was given. Comparing the simulation results of the predicted remaining life of Unscented Particle Filter and Particle Filter with the experimentally measured data through Matlab, the UPF algorithm was selected as the optimal algorithm and the faults were diagnosed in time to provide suggestions for subsequent maintenance.