Precise　capacity　and　state-of-charge　(SOC)　estimation　is　crucial　to　assure　safe　and　reliable　operation　of　lithium-ion　battery.　To　lower　the　influence　of　cross　interference　between　these　two　estimated　states　and　possible　divergence　existing　in　two-way　transmitted　co-estimation　framework,　a　novel　double　adaptive　extended　Kalman　filters　(AEKFs)　based　one-way　transmitted　co-estimation　framework　for　capacity　and　SOC　is　proposed　in　this　paper.　Firstly,　the　model　parameters　of　the　first-order　RC　model　and　open-circuit-voltage　(OCV)　are　online　obtained　by　forgetting　factor　recursive　least　squares.　With　the　first　derivative　of　OCV　versus　SOC,　the　SOC　inferred　through　OCV-SOC　table　and　identified　parameters　are　inputted　into　AEKF1　to　online　estimate　capacity.　Subsequently,　estimated　capacity　is　further　transmitted　into　AEKF2　to　predict　SOC.　By　simulation　driving　cycles,　the　proposed　co-estimation　framework　is　compared　with　AEKF　based　SOC　algorithm　without　capacity　calibration,　whose　results　indicate　that　the　presented　algorithm　can　lower　the　impact　of　inaccurate　initial　capacity　value　on　SOC　estimation　to　more　effectively　track　SOC.　Moreover,　through　robustness　analysis　results,　it　is　clearly　found　that　initial　erroneous　SOC　values　will　not　influence　capacity　estimation　results　due　to　the　one-way　transmitted　characteristic　of　the　proposed　co-estimation　framework　and　SOC　can　still　be　estimated　accurately　and　robustly.　©　2020