This　study　proposes　the　novel　method　of　lithium-ion　battery　state　of　health　(SoH)　estimation　and　remaining　useful　life　(RUL)　prediction　to　ensure　the　safety　and　reliability　of　the　energy　storage　system.　A　fuzzy　brain　emotional　learning　neural　network　(FBELNN)　is　employed　to　estimate　SoH　and　a　recurrent　cerebellar　model　neural　network　(RCMNN)　is　used　for　the　RUL　prediction.　The　inputs　of　FBELNN　are　extracted　features　from　the　monitoring　curve　of　the　constant　voltage　and　current,　because　the　lithium-ion　battery　is　seldom　completely　discharged　and　the　discharging　situation　in　actual　operating　process　is　complex.　The　FBELNN　learns　the　battery　aging　features　that　are　extracted　and　selected　by　discrete　wavelet　transform　and　principal　component　analysis,　respectively.　The　SoH　estimation　results　from　the　FBELNN　are　accurate　due　to　the　special　structure　and　parameters　adaptive　laws.　The　RCMNN　and　online　training　again　can　improve　the　performance　of　RUL　prediction,　because　recurrent　units　can　capture　the　dynamic　features.　Experimental　data　are　performed　by　using　NASA　Prognostics　Center　of　Excellence　battery　datasets　to　verify　the　effectiveness　of　the　proposed　method.　The　results　show　that　the　root　mean　square　error　of　SoH　estimation　is　smaller　by　the　FBELNN　and　the　prediction　accuracy　of　RUL　is　higher　by　RCMNN　under　the　different　starting　points.