Accurate　estimation　and　prediction　of　the　state-of-health　(SOH)　and　remaining　useful　life　(RUL)　of　lithium-ion　batteries　(LIBs)　at　the　early　cycling　stage　are　essential　for　enabling　efficient　battery　recycling,　secondary　utilization,　and　timely　warnings.　However,　the　high　similarity　of　battery　charging　data　during　the　early　cycling　stage,　the　requirement　for　extensive　data　to　assess　aging,　and　the　complexity　of　multidimensional　feature　extraction　pose　significant　challenges　for　existing　predictive　methods.　To　address　these　challenges,　a　novel　framework　based　on　an　optimized　echo　state　network　(ESN)　for　SOH　estimation　and　RUL　prediction　with　early　data　features　is　proposed.　Inspired　by　the　idea　of　phase-space　reconstruction,　the　delay　time　is　employed　to　capture　the　characteristics　of　the　early　voltage　profile.　A　novel　improved　whale　optimization　algorithm　(IWOA)　is　employed　to　optimize　the　ESN,　facilitating　rapid　and　precise　prediction　of　both　SOH　and　RUL.　Experimental　results　showed　that　the　root　mean　square　error　(RMSE)　and　mean　absolute　percentage　error　(MAPE)　for　battery　SOH　can　be　reduced　to　0.33%　and　0.27%,　respectively,　and　the　RMSE　and　MAPE　for　battery　RUL　can　be　reduced　to　0.4%　and　0.43%,　respectively.　By　leveraging　early　cycle　data,　the　proposed　method　not　only　enhances　the　efficiency　and　accuracy　of　SOH　estimation　and　RUL　prediction,　but　also　introduces　a　novel　perspective　for　practical　battery　management　and　predictive　maintenance,　thereby　advancing　the　state-of-the-art　in　battery　health　monitoring　systems.　©　1963-2012　IEEE.