This　study　targets　the　challenge　of　identifying　and　selecting　meaningful　features　in　lithium-ion　batteries　(LIBs)　data　analytics　to　improve　the　accuracy　and　reliability　of　State　of　Health　(SOH)　assessment.　A　total　of　47　battery　health　features　from　existing　studies　are　analyzed,　and　feature　selection　guidelines　are　proposed　to　support　more　accurate　SOH　estimation　under　varying　conditions.　A　Fisher-inspired　feature　selection　(FIFS)　framework　is　introduced,　combining　physical　principles　with　data-driven　modeling.　By　leveraging　the　Fisher　information　matrix　and　convex　optimization,　FIFS　captures　feature　sensitivity,　correlations,　nonlinearity,　and　noise.　Compared　to　traditional　correlation-based　methods,　FIFS　reduces　the　mean　absolute　error　(MAE)　and　root　mean　squared　error　(RMSE)　by　at　least　26.4%　and　21.4%,　respectively,　across　neural　network　architectures.　Additionally,　a　sparrow　search　algorithm　optimized　graph　neural　network　(SSA-GNN)　is　proposed　for　SOH　estimation.　Experiments　on　the　NASA,　UofM,　MIT,　and　Wenzhou　Pack　Degradation　datasets　show　that　SSA-GNN　achieves　minimum　RMSE　values　of　0.341%,　0.106%,　0.208%,　and　0.411%,　respectively,　outperforming　advanced　models.　Compared　to　vanilla　GNNs,　SSA-GNN　reduces　MAE　and　RMSE　by　up　to　29.9%　and　24.0%.　This　work　offers　a　robust　framework　for　LIBs,　enhancing　estimation　accuracy　and　model　generalization　through　effective　feature　selection　and　automated　optimization.