Lithium-ion　batteries　(LIBs)　are　widely　employed　in　electric　vehicles　(EVs)　and　energy　storage　systems　owing　to　their　high　energy　density,　low　self-discharge　rate,　and　superior　longevity.　As　a　vital　evaluation　index　for　the　degradation　state　of　LIBs,　the　state　of　health　(SOH)　must　be　accurately　estimated.　This　study　adopts　an　electrochemical　impedance　spectroscopy　(EIS)-based　equivalent　circuit　model　(ECM)　to　estimate　battery　SOH.　The　internal　dynamic　electrical　behaviors　of　LIBs　are　decoupled　by　the　distribution　of　relaxation　times　(DRT)　method,　whereby　the　critical　degradation　features　are　extracted　from　the　DRT　curves　and　the　fitted　ECM　parameters.　Subsequently,　referring　to　the　ensemble　learning　approach　(ELA),　the　multiple　correlation　analysis　(MCA)　is　utilized　to　identify　the　most　pertinent　degradation　features.　The　battery　data　are　subjected　to　a　comprehensive　analysis　with　five　types　of　correlations　to　ascertain　the　optimal　number　and　combination　of　health　indicators　(HIs).　Furthermore,　the　weighted　mean　of　vectors　(INFO)　algorithm　is　employed　to　optimize　the　hyper-　parameters　in　the　echo　state　network　(ESN)　model　for　SOH　estimation.　The　echo　state　networks　model　optimized　with　the　weighted　mean　of　vectors　algorithm　(INFO-ESN)　is　verified　with　eight　types　of　LIBs　under　different　operating　conditions.　Experimental　results　of　the　proposed　method　manifest　that　the　root　mean　square　error　(RMSE)　and　mean　absolute　error　(MAE)　of　the　battery　SOH　range　from　0.32　%　to　1.06　%,　thereby　verifying　the　accuracy　and　robustness　of　the　proposed　method.