In　this　paper,　we　consider　composite　quantile　estimation　for　the　partial　functional　linear　regression　model　with　errors　from　a　short-range　dependent　and　strictly　stationary　linear　processes.　The　functional　principal　component　analysis　method　is　employed　to　estimate　the　slope　function　and　the　functional　predictive　variable,　respectively.　Under　some　regularity　conditions,　we　obtain　the　optimal　convergence　rate　of　the　slope　function,　and　the　asymptotic　normality　of　the　parameter　vector.　Simulation　studies　demonstrate　that　the　proposed　new　estimation　method　is　robust　and　works　much　better　than　the　least　squares　based　method　when　there　are　outliers　in　the　dataset　or　the　autoregressive　error　distribution　follows　a　heavy-tailed　distribution.　Finally,　we　apply　the　proposed　methodology　to　electricity　consumption　data.