Aiming　at　the　problems　of　time-varying　battery　parameters　and　inaccurate　estimations　of　state　of　charge　(SOC)　and　state　of　health　(SOH),　a　joint　estimation　algorithm　of　SOC　and　SOH　is　proposed.　A　particle　filter　algorithm　is　used　to　identify　the　parameters　online　on　the　basis　of　a　second-order　equivalent　circuit　model.　The　algorithm　feasibility　is　verified　through　the　terminal　voltage　estimation　accuracy.　Considering　that　an　accurate　SOH　is　one　of　the　foundations　to　achieve　an　accurate　SOC　estimation,　a　dual　particle　filter　is　used　to　jointly　estimate　SOC　and　SOH.　Under　different　test　conditions,　the　effect　of　different　initial　values　(initial　SOC　and　capacity),　temperatures,　operation　conditions,　particle　number,　and　model　parameters　on　the　estimation　accuracy　and　robustness　is　compared　and　analyzed.　The　effectiveness　of　the　proposed　algorithm　is　validated　by　experimental　data　under　different　operation　conditions.　Experimental　results　show　that　the　online　particle　filter　algorithm　can　well　predict　the　dynamic　battery　model　parameters.　The　proposed　algorithm　has　high　robustness　and　a　good　tracking　effect　when　estimating　SOC　with　a　mean　absolute　error　of　less　than　1.3%,　a　root　mean　square　error　of　less　than　1%,　and　a　tracking　terminal　voltage.