Aiming at the longitudinal force control problem of pure electric pickup trucks under low-speed creep conditions, in order to improve control accuracy, driving smoothness and anti-disturbance robustness, this paper proposes a dynamic constraint model predictive control strategy integrating real-time slope feedforward. Firstly, establish a discrete state space model of the vehicle's longitudinal dynamics considering multi-source drag coupling; Secondly, a multi-objective cost function is constructed with speed tracking error, torque change rate and energy consumption as the optimization objectives. The core innovation lies in the design of a dynamic constraint adjustment mechanism based on slope feedforward. By estimating the slope resistance in real time and dynamically correcting the motor torque constraint boundary, the robustness of the controller under large slope disturbances and the feasibility of solving optimization problems are enhanced. Finally, a real vehicle test platform was built based on domestic pure electric pickup trucks to complete the verification. The results show that compared with the traditional proportional-integral-differential controller,the proposed MPC controller,reduces the average absolute error of speed tracking by 53.1% and the fluctuation of torque change rate (characterizing driving smoothness) by 52.4%. It shows excellent dynamic response characteristics under both slope and load change conditions, which can significantly improve the comprehensive control quality under creep conditions and has engineering application value.