A　novel　hierarchical　direct　yaw　moment　controller　is　designed　to　enhance　the　lateral　stability　of　the　four-wheel-drive　electric　vehicle.　The　adaptive　sliding　mode　control　(ASMC)　technique　in　the　upper-layer　controller　is　employed　to　compute　an　additional　yaw　moment.　The　lower-layer　controller　distributes　this　yaw　moment　into　each　independent　wheel　by　utilizing　model　predictive　control　allocation　(MPCA).　The　proposed　MPCA　aims　to　mitigate　the　performance　deterioration　induced　by　in-wheel　motor　dynamics　and　optimize　the　power　consumption　stemming　from　the　additional　yaw　moment.　Co-simulation　and　hardware-in-the-loop　(HIL)　test　is　conducted　to　verify　the　performance　of　the　proposed　controller.　Validation　results　show　that　the　proposed　hierarchical　ASMC-MPCA　controller　outperforms　the　sliding　mode　control　MPCA　(SMC-MPCA)　and　the　integrated　nonlinear　model　predictive　control　(NMPC)　with　the　lowest　root-mean-square　errors　(　RMSE　s　)　of　yaw　rate,　sideslip　angle,　lateral　deviation,　and　lowest　power　consumption.　Additionally,　the　chattering　phenomenon　in　SMC-MPCA　can　be　suppressed　effectively　by　adaptively　estimating　the　parameter　uncertainties.　The　proposed　ASMC-MPCA　controller　also　consumes　less　computational　resources　than　the　NMPC　and　SMC-MPCA,　which　indicates　that　the　ASMC-MPCA　is　more　suitable　for　an　automotive　onboard　controller.　The　comparison　between　hierarchical　and　integrated　controller　frameworks　also　shows　that　the　hierarchical　framework　is　more　suitable　for　production　vehicles　under　non-powerful　vehicle　control　units.