针对纯电动皮卡在低速蠕行工况下的纵向力控制问题，为提升控制精度、驾驶平顺性及抗扰动鲁棒性，文章提出一种融合实时坡度前馈的动态约束模型预测控制策略。首先，建立考虑多源阻力耦合的整车纵向动力学离散状态空间模型。其次，构建以速度跟踪误差、扭矩变化率及能量消耗为优化目标的多目标代价函数，其核心创新在于设计基于实时坡度前馈的动态约束调整机制，通过实时估算坡道阻力，动态修正电机扭矩约束边界，增强控制器在大坡度扰动下的鲁棒性与优化问题求解可行性。最后，基于国产纯电皮卡搭建实车测试平台完成验证。结果表明：与传统比例 - 积分 - 微分控制器相比，所提 MPC 控制器将速度跟踪平均绝对误差降低 53.1%，扭矩变化率波动（表征驾驶平顺性）降低 52.4%，在坡道与载荷变化工况下均表现出优异的动态响应特性，可显著提升蠕行工况综合控制品质，具备工程应用价值。

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.