Accurate　monitoring　of　a　battery＇s　state　of　health　(SOH)　is　crucial　for　ensuring　reliable　operation.　Data-driven　methods　for　SOH　estimation　often　involve　complex　feature　extraction　strategies　and　models　that　are　difficult　to　interpret,　limiting　their　generalizability.　To　overcome　these　challenges,　this　paper　presents　a　battery　SOH　estimation　method　based　on　the　refined　composite　multiscale　Hilbert　cumulative　residual　entropy　algorithm　(RCMHCRE)　and　the　battery　physical　information　neural　network　(BatteryPINN).　First,　the　proposed　RCMHCRE　algorithm　is　applied　to　automatically　extract　high-quality　health　features　from　the　battery＇s　voltage　and　current　data,　serving　as　the　feature　engineering　in　this　study.　Second,　the　network　structure　of　BatteryPINN　is　developed　for　SOH　prediction,　based　on　the　mathematical　theory　of　solid　electrolyte　interphase　(SEI)　membrane　growth.　The　proposed　strategy　enables　BatteryPINN　to　be　constrained　by　the　battery　aging　mechanism　during　training,　thereby　ensuring　that　the　network　adheres　to　the　underlying　physical　laws　during　propagation.　To　validate　the　effectiveness　of　the　proposed　method,　a　four-month　battery　aging　experiment　is　conducted,　and　a　dataset　is　constructed.　Experimental　results　from　three　datasets　demonstrate　that　the　proposed　method　offers　significant　advantages　in　health　feature　extraction　and　SOH　estimation　compared　to　other　state-of-the-art　battery　SOH　estimation　methods,　achieving　prediction　accuracies　of　less　than　1%　for　both　RMSE　and　MAPE　metrics.