This　paper　proposes　a　new　approach　for　variable　selection　in　partially　linear　errors-in-variables　(EV)　models　for　longitudinal　data　by　penalizing　appropriate　estimating　functions.　We　apply　the　SCAD　penalty　to　simultaneously　select　significant　variables　and　estimate　unknown　parameters.　The　rate　of　convergence　and　the　asymptotic　normality　of　the　resulting　estimators　are　established.　Furthermore,　with　proper　choice　of　regularization　parameters,　we　show　that　the　proposed　estimators　perform　as　well　as　the　oracle　procedure.　A　new　algorithm　is　proposed　for　solving　penalized　estimating　equation.　The　asymptotic　results　are　augmented　by　a　simulation　study.