Liquid　metal　battery　is　a　new　battery　with　high　current　charging　and　discharging　capability,　low　cost　and　long　service　life.　It　has　a　large　capacity　and　is　suitable　to　be　used　in　power　grid.　An　accurate　online　identification　of　battery　model　parameters　is　the　basis　of　the　state　of　charge　and　state　of　health　estimation.　However,　there　is　presently　no　published　literature　for　the　on-line　estimation　of　the　parameters　in　the　liquid　metal　battery　model.　To　improve　the　suitability　of　liquid　metal　battery　model　under　various　scenarios,　such　as　fluctuating　and　SoC　variation,　dynamic　model　with　parameters　updated　on-time　is　developed,　based　on　second　order　RC　model,　using　bias　compensation　recursive　least　squares　method　with　forgetting　factor　(FF-BCRLS).　Open　circuit　voltage　(OCV)　of　this　device　is　also　estimated　as　a　parameter　of　the　model.　Three　designed　working　scenarios　are　adopted　to　examine　the　performance　of　the　algorithm　and　general　recursive　least　squares　method　is　used　as　a　comparison.　The　root　mean　square　error　and　the　mean　relative　error　of　the　estimation　using　this　algorithm　is　less　than　0.01　V　and　0.16%,　both　less　than　that　using　general　RLS　algorithm.　The　parameters　of　the　battery,　internal　resistance,　polarization　capacitances　and　resistances,　and　OCV,　are　proved　to　be　obtained　easily　and　accurately　and　time-varying　by　this　algorithm,　and　the　maximum　estimation　error　of　the　OCV　is　about　0.07　V.　The　algorithm　has　of　high　accuracy　and　good　adaptability　to　different　battery　conditions.　©　2018　IEEE.