Accurate　estimation　of　the　state　of　health　(SOH)　of　Li-ion　batteries　is　an　important　guarantee　to　ensure　safe　and　reliable　operation　of　battery　systems,　and　is　also　a　key　indicator　for　battery　management　systems.　In　this　paper,　a　method　for　SOH　estimation　of　lithium-ion　batteries　based　on　double-stage　attention-based　gated　recurrent　unit　(DAGRU)　is　proposed.　Firstly,　depthwise　separable　convolution　(DSC)　was　used　to　extract　the　health　features　(HFs)　of　battery　charging　voltage.　Secondly,　the　model　uses　double-stage　attention,　which　enables　more　information　on　input　features　to　be　obtained　at　both　the　temporal　and　spatial　scales.　Finally,　NASA　battery　data　set　was　used　to　verify　the　proposed　method.　Experimental　results　show　that　the　proposed　method　can　accurately　estimate　the　SOH　of　lithium-ion　batteries.　©　2023　SPIE.