Hydrogen supply and circulating in vehicular fuel cells is crucial for their output capability and lifetime. In this article, an enhanced multiple-input multiple-output (MIMO) model predictive control (MPC) scheme is proposed for hydrogen regulation based on vehicle speed-induced fuel cell current disturbance stochastic prediction. The Markov exponential smoothing law is first developed for the vehicle speed prediction. The forecasted fuel cell power demand is obtained through vehicle dynamics model and rule-based energy management to release the predictive stack current regarding as the disturbance of hydrogen control system. The discrete predicted current sequence is with stochastic features and typed into the predictive model of MPC which is on longer the length of control horizon. Two case studies are presented to discuss the influence of different speed sampling times on the hydrogen regulation result under the proposed enhanced MPC. The enhanced MPC has a better performance than the traditional MPC, and the control RMSE of which can be reduced by 44.09% in case 1 and 69.78% in case 2 during automotive driving cycles. A dSPACE MicroAutoBox hardware in loop (HIL) experiment was conducted and the results well matched with the simulation which has verified the real-time performance of the enhanced MPC scheme.

车辆燃料电池中的氢气供应和循环对于其输出能力和使用寿命至关重要。在本文中，基于车辆速度引起的燃料电池电流扰动随机预测，提出了一种用于氢调节的增强型多输入多输出（MIMO）模型预测控制（MPC）方案。马尔可夫指数平滑定律首先用于车辆速度预测。通过车辆动力学模型和基于规则的能量管理来获得预测的燃料电池功率需求，以释放与氢控制系统有关的预测堆电流。离散的预测电流序列具有随机特征，并输入到MPC的预测模型中，该模型具有较长的控制范围。提出了两个案例研究，以讨论在建议的增强型MPC下，不同速度采样时间对氢调节结果的影响。增强型MPC具有比传统MPC更好的性能，在汽车驾驶周期中，其控制RMSE在情况1下可以降低44.09％，在情况2下可以降低69.78％。进行了dSPACE MicroAutoBox硬件在环（HIL）实验，其结果与仿真非常吻合，已验证了增强型MPC方案的实时性能。