The　residual　life　is　one　of　important　performance　of　lithium-ion　battery.　Before　the　life　prediction,　the　SoH　(State　of　Health)　data　of　lithium-ion　battery　are　necessary　to　be　available.　In　order　to　improve　the　accuracy　of　SoH　estimation,　electrolyte　dynamics　is　added　to　the　single　particle　model　of　lithium-ion　battery　in　this　paper.　Then,　a　novel　Pade　approximation　and　least　squares　method　are　employed　to　estimate　the　SoH　of　lithium-ion　batteries.　After　that,　the　mapping　particle　filter　is　applied　to　forecast　the　battery　life.　MPF　can　greatly　improve　the　diversity　of　particles　and　avoid　the　operation　of　resampling.　This　is　the　first　time　that　the　mapping　particle　filter　has　been　used　to　forecast　the　residual　life　of　lithium-ion　batteries.　Finally,　the　experimental　data　from　National　Aeronautics　and　Space　Administration　is　used　to　prove　that　the　mapping　particle　filter　has　a　higher　precision　of　prediction　than　the　standard　particle　filter.