Accurate　estimation　of　the　state　of　health　(SOH)　of　lithium-ion　batteries　is　an　important　guarantee　to　ensure　safe　and　reliable　operation　of　lithium-ion　battery　systems.　However,　the　complex　aging　mechanism　inside　the　battery　makes　it　difficult　to　measure　the　battery　SOH　directly.　In　this　paper,　a　SOH　estimation　method　based　on　a　novel　dual-stage　attention-based　recurrent　neural　network　(DARNN)　and　health　feature　(HF)　extraction　from　time　varying　charging　process　is　proposed.　Firstly,　the　constant　current　charging　time,　the　maximum　temperature　time,　the　isochronous　voltage　difference,　and　the　isochronous　current　were　extracted　as　lithium-ion　battery　HFs,　and　their　correlations　with　SOH　are　verified　by　spearman　correlation　coefficient.　Secondly,　the　DARNN　is　proposed　to　capture　the　time-dependent　and　temporal　features　of　the　input　sequence　and　to　accurately　predict　SOH.　Finally,　the　proposed　estimation　method　is　validated　on　the　NASA　battery　dataset.　The　results　show　that　the　method　can　accurately　estimate　SOH　for　lithium-ion　batteries.　The　mean　square　error　and　the　mean　absolute　percentage　error　of　the　method　are　＜0.5　%.　©　2023　Elsevier　Ltd