Accurate　online　estimation　of　the　state　of　charge　(SOC)　of　the　lithium　battery　can　effectively　extend　the　battery　life　and　improve　the　safety　of　the　battery,　which　is　very　important　for　the　battery　management　system　(BMS)　of　the　electric　vehicle.　Aiming　at　the　problem　of　slow　convergence　in　the　initial　running　of　the　adaptive　extended　Kalman　filter　(AEKF)　algorithm,　this　paper　proposes　a　fuzzy　AEKF　(FAEKF)　algorithm　to　improve　the　convergence　speed　of　the　AEKF　algorithm.　Taking　the　absolute　value　of　the　difference　between　the　actual　terminal　voltage　and　the　predicted　terminal　voltage　of　the　NCR18650B　ternary　lithium　battery　and　its　change　rate　as　the　fuzzy　input,　using　the　noise　measured　R　in　the　Kalman　filter　system　as　the　fuzzy　output,　and　adjusting　the　gain　K　of　the　algorithm　by　fuzzy　controlling　R　in　the　iterative　process,　then　realize　the　fuzzy　adjustment　of　the　convergence　speed.　Experimental　results　show　that　compared　with　the　extended　Kalman　(EKF)　and　AEKF　algorithms　under　the　conditions　of　0.5C　rate　constant　current　discharge　condition　and　dynamic　stress　test　condition　(DST),　the　improved　algorithm　can　improve　the　convergence　speed,　while　not　reduce　the　estimation　accuracy,　which　is　more　practical　in　the　online　estimation　of　SOC.　©　2020,　Electrical　Technology　Press　Co.　Ltd.　All　right　reserved.