Lithium-ion　batteries　are　widely　used　as　the　power　source　in　electric　vehicles.　The　state　of　health　(SOH)　diagnosis　is　very　important　for　the　safety　and　storage　capacity　of　lithium-ion　batteries.　In　order　to　accurately　and　robustly　estimate　lithium-ion　battery　SOH,　a　novel　long　short-term　memory　network　(LSTM)　based　on　the　charging　curve　is　proposed　for　SOH　estimation　in　this　work.　Firstly,　aging　features　that　reflect　the　battery　degradation　phenomenon　are　extracted　from　the　charging　curves.　Then,　considering　capture　the　long-term　tendency　of　battery　degradation,　some　improvements　are　made　in　the　proposed　LSTM　model.　The　connection　between　the　input　gate　and　the　output　gate　is　added　to　better　control　output　information　of　the　memory　cell.　Meanwhile,　the　forget　gate　and　input　gate　are　coupled　into　a　single　update　gate　for　selectively　forgetting　before　the　accumulation　of　information.　To　achieve　more　reliability　and　robustness　of　the　SOH　estimation　method,　the　improved　LSTM　network　is　adaptively　trained　online　by　using　a　particle　filter.　Furthermore,　to　verify　the　effectiveness　of　the　proposed　method,　we　compare　the　proposed　method　with　two　data-driven　methods　on　the　public　battery　data　set　and　the　commercial　battery　data　set.　Experimental　results　demonstrate　the　proposed　method　can　obtain　the　highest　SOH　accuracy.