Accurately　estimating　battery　capacity　plays　a　crucial　role　in　determining　the　State　of　Health　(SOH)　of　lithium-ion　batteries,　which　is　essential　for　ensuring　their　safe　operation　and　protection.　This　paper　proposes　a　Stacking　ensemble　model　based　on　feature　fusion　using　Principal　Component　Analysis　(PCA)　for　battery　capacity　estimation.　Multiple　health　factors　are　extracted　from　the　battery　testing　data,　and　use　PCA　to　fuse　the　health　factors　to　reduce　the　computational　complexity　of　the　model.　In　view　of　the　performance　difference　of　a　single　model　on　different　datasets　data　sets,　this　paper　proposes　a　new　Stacking　ensemble　model　that　utilizes　different　model　stacks　to　complement　each　other＇s　strengths.　The　Stacking　model　improves　the　generalization　ability　and　stability　of　the　model　on　different　datasets　by　using　ridge　regression　to　fuse　three　heterogeneous　base　models.　This　method　is　validated　on　the　NASA　battery　dataset,　and　by　comparing　the　errors　of　the　base　models　and　other　ensemble　methods　across　different　training　data　ratios,　the　Stacking　model　has　the　smallest　error　across　all　datasets,　it　is　demonstrated　that　the　Stacking　ensemble　model　has　significant　advantages　in　terms　of　accuracy　and　generalization　ability.　The　capacity　estimation　results　for　the　four　datasets　show　that　the　Stacking　model　achieved　error　of　less　than　0.015Ah.　The　average　absolute　error　and　root　mean　square　error　of　the　dataset　with　the　smallest　error　are　0.0025Ah　and　0.0031Ah,　respectively.　The　estimation　results　indicate　that　the　Stacking　ensemble　model　has　higher　accuracy　and　robustness　in　estimating　battery　capacity　compared　to　other　data-driven　and　ensemble　methods.　©　2024　Elsevier　Ltd