Battery　state　of　health　(SOH)　is　a　momentous　indicator　for　aging　severity　recognition　of　lithium-ion　batteries　and　is　also　an　indispensable　parameter　of　the　battery　management　system.　In　this　paper,　an　innovative　SOH　estimation　algorithm　based　on　feature　transfer　is　proposed　for　lithium-ion　batteries.　Firstly,　sequence　features　with　battery　aging　information　are　sufficiently　extracted　based　on　the　capacity　increment　curve.　Secondly,　transfer　component　analysis　is　employed　to　obtain　the　mapping　that　minimizes　the　data　distribution　difference　between　the　training　set　and　the　test　set　in　the　shared　feature　space.　Finally,　the　generalized　additive　model　is　investigated　to　estimate　the　battery　health　status.　The　experimental　results　demonstrate　that　the　proposed　algorithm　is　capable　of　forecasting　the　SOH　for　lithium-ion　batteries,　and　the　results　are　more　outstanding　than　those　of　several　comparison　algorithms.　The　predictive　error　evaluation　indicators　for　each　battery　are　both　less　than　2.5%.　In　addition,　satisfactory　SOH　estimation　results　can　also　be　obtained　by　only　relying　on　a　small　amount　of　data　as　the　training　set.　The　comparative　experiments　using　traditional　features　and　different　machine　learning　methods　also　testify　to　the　superiority　of　the　proposed　algorithm.