In　this　paper,　a　new　estimation　procedure　based　on　composite　quantile　regression　and　functional　principal　component　analysis　(PCA)　method　is　proposed　for　the　partially　functional　linear　regression　models　(PFLRMs).　The　proposed　estimation　method　can　simultaneously　estimate　both　the　parametric　regression　coefficients　and　functional　coefficient　components　without　specification　of　the　error　distributions.　The　proposed　estimation　method　is　shown　to　be　more　efficient　empirically　for　non-normal　random　error,　especially　for　Cauchy　error,　and　almost　as　efficient　for　normal　random　errors.　Furthermore,　based　on　the　proposed　estimation　procedure,　we　use　the　penalized　composite　quantile　regression　method　to　study　variable　selection　for　parametric　part　in　the　PFLRMs.　Under　certain　regularity　conditions,　consistency,　asymptotic　normality,　and　Oracle　property　of　the　resulting　estimators　are　derived.　Simulation　studies　and　a　real　data　analysis　are　conducted　to　assess　the　finite　sample　performance　of　the　proposed　methods.　(C)　2018　The　Korean　Statistical　Society.　Published　by　Elsevier　B.V.　All　rights　reserved.