A　novel　dual-motor　electric　vehicle　(DMEV)　with　superior　energy　efficiency　depends　on　its　diverse　operation　modes.　However,　the　operation　mode　shift　will　lead　to　the　ride　comfort　problem　owing　to　the　uncertainty　of　the　actual　driving　cycles.　To　address　this　issue,　bi-objective　optimization　strategy-based　driving　cycle-aware　bias　coefficients　is　proposed　to　tradeoff　between　energy　consumption　and　shift　shock.　Firstly,　the　system　efficiency　and　the　shift　shock　models　are　developed　for　DMEV.　The　torque　distribution　coefficient　is　defined　to　be　a　control　variable　for　the　bi-objective　optimization　of　system　efficiency　and　ride　comfort.　Furthermore,　this　study　combines　the　bi-objective　optimization　algorithm　with　driving　cycle　recognition　to　introduce　the　optimized　bias　coefficient　to　determine　the　driving　cycle　aware　bias　coefficient　for　the　mentioned　optimized　target　objectives.　Then,　the　non-dominated　sorting　genetic　algorithm-II　is　applied　for　the　bi-objective　optimization,　which　produces　a　group　of　the　Pareto　solutions　including　the　energy　consumption　and　shift　shock.　Moreover,　the　proposed　bi-objective　optimization　strategy　is　conducted　by　controlling　the　optimized　bias　lines　for　the　optimal　torque　distribution　of　the　novel　dual-motor　powertrain　system.　Ultimately,　numerous　validations　and　comparisons　demonstrate　that　the　proposed　strategy　effectively　accomplishes　the　trade-off　optimization　between　energy　consumption　and　shift　shock　in　real-time.(c)　2022　Elsevier　Ltd.　All　rights　reserved.