The　state　of　charge　(SOC)　of　lithium　battery　is　a　key　parameter　for　effective　management　of　battery　management　systems　(BMS).　To　address　the　problems　of　low　precision,　complex　computation　and　poor　robustness　of　traditional　charging　state　estimation　methods,　an　enhanced　algorithm　based　on　Unscented　Kalman　filter　(UKF)　is　proposed.　The　singular　value　decomposition　(SVD)　method　is　adopted　to　ensure　the　normal　operation　of　the　Unscented　Kalman　Filter　(UKF)　algorithm　even　when　the　matrix　P　lacks　positive　semi-definiteness　from　a　mathematical　perspective.　This　enhancement　significantly　improves　the　theoretical　robustness　of　UKF.　Schmidt　orthogonal　transformation　is　concurrently　used　to　reduce　the　computational　complexity　in　the　sampling　point　selection　process,　and　the　multi-innovation　theory　is　combined　with　adaptive　noise　control　to　further　improve　the　accuracy　of　SOC　estimation.　The　algorithm　is　validated　using　the　Urban　Dynamometer　Driving　Schedule　(UDDS)　condition.　The　simulation　results　are　excited　using　Singular　value　decomposition-multi-innovation　adaptive　Schmidt　orthogonal　unscented　Kalman　filter　method　(SVD-MIASOUKF).　0.95　%　and　1.29　%　of　maximum　errors　are　obtained　at　25　degrees　C　and　-10　degrees　C,　while　the　maximum　errors　are　2.46　%-2.99　%　using　SVD-UKF　and　UKF.　The　proposed　approach　shows　faster　convergence　speed　and　higher　estimation　accuracy　in　comparison　to　traditional　algorithms.