Vehicle　velocity　prediction　is　of　great　significance　in　electric　vehicle　(EV)　energy　consumption　evaluation.　However,　vehicle　velocity　prediction　is　complicated　due　to　the　uncertainty　of　vehicle　velocity　and　driving　pattern.　To　address　the　challenges,　a　vehicle　velocity　prediction　model　based　on　driving　pattern　recognition　(DPR)　and　Markov　Chain　(MC)　is　proposed.　Firstly,　three　typical　driving　cycles　are　adopted　to　constructed　sample　driving　cycle.　K-means　algorithm　is　used　for　clustering　the　constructed　driving　cycle　segments.　And　Learning　Vector　Quantization　(LVQ)　neural　network　(NN)　is　applied　to　recognizing　the　driving　pattern　in　real-time,　then　the　Markov　Transition　Matrix　(MTM)　corresponding　to　three　clustered　driving　patterns　are　adopted　to　predict　vehicle　velocity.　Finally,　the　velocity　prediction　results　are　applied　to　dual-motor　EV　energy　consumption　evaluation　model　which　is　established　by　Multiple　Linear　Regression　(MLR).　Velocity　prediction　results　show　that　the　Root　Mean　Square　Error　(RMSE)　value　of　velocity　prediction　based　on　DPR　and　MC　decreased　by　24.1%　compared　with　MC　prediction　without　DPR.　The　velocity　prediction　is　applied　to　energy　consumption　evaluation,　the　results　shows　that　the　error　is　2.33%,　which　is　sufficient　to　demonstrate　the　accuracy　of　the　velocity　prediction　model　and　the　energy　consumption　evaluation　model.　©　2020　Elsevier　B.V.