Aiming　at　the　shortcomings　of　unscented　Kalman　filter　(UKF)　algorithm　for　estimating　lithium　battery　state　of　charge　(SOC)　due　to　low　accuracy,　poor　stability,　too　many　sigma　points　generated,　and　calculation　difficulty,　an　algorithm　of　unscented　Kalman　filter　based　on　adaptive　spherical　insensitive　transformation　(ASIT-UKF)　was　proposed,　and　the　spherical　insensitive　transformation　method　was　used　to　select　weight　coefficients　and　initialization　line　unary　vector　to　select　the　generation　of　sigma　points.　Compared　with　the　UKF　algorithm,　the　ASIT-UKF　algorithm　was　reduced　nearly　50%　fewer　sigma　points,　so　that　the　computational　complexity　of　the　algorithm　was　significantly　reduced.　At　the　same　time,　all　the　generated　sigma　points　were　normalized　on　the　unit　spherical　surface　to　improve　numerical　stability.　Considering　the　uncertainty　of　noise　interference　in　the　actual　operation　of　lithium　battery　system,　Sage-Husa　adaptive　filter　was　added　to　update　and　correct　the　interference　of　uncertain　noise　in　real　time,　so　as　to　improve　the　accuracy　of　online　lithium　battery　SOC　estimation.　Finally,　the　standard　deviation　and　mean　deviation　calculation　formulas　were　introduced　into　the　estimated　performance　indicators.　Results　show　that　the　ASIT-UKF　algorithm　has　superior　performance　in　terms　of　accuracy,　robustness　and　convergence.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.