With　the　widespread　application　of　lithium-ion　batteries　in　electric　vehicles,　accurately　estimating　their　state　of　health　(SOH)　has　become　a　key　focus　of　research.　This　paper　explores　various　feature　optimization　methods　and　data-driven　models　with　different　structures,　and　constructs　various　SOH　estimation　models　suitable　for　lithiumion　batteries.　Based　on　battery　testing　data,　multiple　features　are　extracted　from　voltage　and　temperature　to　characterize　the　battery　aging　process.　To　reduce　information　redundancy　among　features,　filtering　methods,　Principal　Component　Analysis　(PCA),　and　Multi-dimensional　Scaling　(MDS)　are　applied　for　optimization,　aiming　to　maximize　feature　information　utilization.　This　paper　compares　four　common　and　structurally　different　datadriven　models:　linear　regression　(LR),　Gaussian　process　regression　(GPR),　support　vector　regression　(SVR),　and　long　short-term　memory　(LSTM)　networks.　The　effectiveness　of　each　model　is　validated　using　multi-feature　inputs,　and　a　multi-dimensional　assessment　of　feature　selection　and　data-driven　model　performance　in　SOH　estimation　is　conducted,　the　average　absolute　error　of　all　models　under　60　%　training　set　conditions　is　0.8　%.　The　average　absolute　error　of　estimating　the　four　batteries　using　the　fused　PCA　features　as　input　and　the　GPR　model　is　less　than　1.2　%.　At　the　same　time,　using　the　optimized　features　as　input　reduces　the　average　training　time　by　46.63　%　compared　to　using　multiple　features　as　input.　In　summary,　the　combination　of　PCA　features　and　GPR　models　has　good　performance　in　both　estimation　accuracy　and　computational　efficiency　for　different　batteries.