Traffic　energy　consumption　estimation　is　significant　for　the　sustainable　transportation.　However,　it　is　difficult　to　directly　employ　macro　traffic　flow　data　to　accurately　estimate　the　traffic　energy　consumption　due　to　many　traffic　energy　consumption　models　need　second-by-second　vehicle　trajectory.　To　solve　this　problem,　this　paper　proposes　a　traffic　energy　consumption　model　based　on　the　macro-micro　data,　which　the　macro　data　derived　from　the　fixed-location　sensors　and　sparse　micro　data　derived　from　the　Connected　and　Automated　Vehicles　(CAVs).　The　completed　vehicle　trajectories　are　constructed　by　the　nonparametric　kernel　smoothing　algorithm　and　variational　theory.　To　test　the　performance　of　the　proposed　method,　the　Next　Generation　Simulation　micro　(NGSIM)　dataset　and　Caltrans　Performance　Measurement　System　macro　dataset　obtained　from　the　same　road　and　time　are　used.　The　results　indicate　that　the　proposed　method　not　only　can　reflect　the　characteristics　of　traffic　kinematic　waves　caused　by　traffic　congestion,　but　also　minimize　the　errors　generated　by　the　macro-micro　transformation.　In　addition,　it　can　significantly　improve　the　accuracy　of　energy　consumption　estimation.