Lithium-ion　battery　disposal　is　becoming　an　increasingly　important　issue　with　the　rapid　growth　of　Electric　Vehicles　(EVs)　regarding　resource　conservation　and　environmental　sustainability.　It　is　considered　the　most　suitable　solution　to　reuse　rather　than　dispose　of　retired　batteries.　However,　the　precision　in　estimating　the　battery　states　is　of　great　importance　to　ensure　the　operational　safety　and　efficiency　of　reused　battery　packs.　This　study　proposes　a　joint　estimation　method　to　predict　the　State　of　Charge　(SOC)　and　the　peak　power　capability　for　reused　battery　packs　considering　inconsistency.　The　primary　content　of　this　work　is　described　as　follows.　(1)　This　paper　designs　an　improved　screening　method　for　evaluating　the　consistency　of　the　reused　batteries　that　are　used　to　connect　to　the　series　battery　pack.　(2)　A　second-order　RC　model　is　selected　as　the　cell　mean　model　(CMM)　to　represent　the　overall　performance　of　the　reused　battery　pack.　On　this　basis,　the　mean　SOC　is　estimated　by　using　Sage-Husa　adaptive　algorithm　and　extended　Kalman　filter　(SH-AEKF),　whereas　the　peak　power　capability　is　evaluated　by　considering　multiple　limitations.　(3)　An　experiment　is　conducted　to　evaluate　the　robustness　of　the　joint　estimation　method.　The　results　show　that　the　maximum　absolute　error　of　SOC　estimation　is　below　+/-　3　%　while　the　mean　absolute　percentage　error　(MAPE)　of　peak　power　capability　estimation　could　be　limited　to　less　than　3.5　%.　This　study　indicates　the　high　accuracy　and　reliability　of　the　proposed　joint　estimation　method　for　retired　battery　packs.