In　this　paper,　we　develop　a　dynamic　partially　functional　linear　regression　model　in　which　the　functional　dependent　variable　is　explained　by　the　first　order　lagged　functional　observation　and　a　finite　number　of　real-valued　variables.　The　bivariate　slope　function　is　estimated　by　bivariate　tensor-product　B-splines.　Under　some　regularity　conditions,　the　large　sample　properties　of　the　proposed　estimators　are　established.　We　investigate　the　finite　sample　performance　of　the　proposed　methods　via　Monte　Carlo　simulation　studies,　and　illustrate　its　usefulness　by　the　analysis　of　the　electricity　consumption　data.