Monitoring　the　performance　of　lithium-ion　batteries　is　crucial　for　the　manufacture　and　operation　of　various　industrial　applications.　The　state　of　health　shows　the　health　status　of　the　batteries.　This　paper　proposes　a　novel　method　for　fast　estimation　of　battery　state　of　health　by　leveraging　response　characteristics　during　load　surges　and　optimizing　the　process　through　a　genetic　algorithm-extreme　learning　machine　model.　Traditional　estimation　techniques　often　rely　on　complete　charge/discharge　profiles,　which　are　inefficient　for　online　monitoring　and　real-time　applications.　The　proposed　method　extracts　key　features　from　voltage　response　curves　during　inrush　currents,　thereby　eliminating　the　need　for　full　charge/discharge　data.　Techniques　such　as　discrete　wavelet　transform　and　differential　voltage　analysis　are　employed　to　capture　vital　health　indicators.　The　genetic　algorithm-extreme　learning　machine　algorithm　significantly　reduces　computational　complexity　while　ensuring　high　estimation　accuracy　by　optimizing　the　parameters　of　the　extreme　learning　machine.　Experimental　results　demonstrate　that　the　model　achieves　high　accuracy　(within　3%)　in　estimating　state　of　health　across　various　states　of　charge.　This　method　is　particularly　suitable　for　applications　requiring　rapid　and　non-invasive　battery　health　assessments,　such　as　backup　power　systems　and　electric　vehicle　startups.　　©　1972-2012　IEEE.