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Disturbance prediction-based enhanced stochastic model predictive control for hydrogen supply and circulating of vehicular fuel cells

机译:基于干扰预测的增强随机模型预测控制,用于氢气供应和车辆燃料电池循环

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摘要

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.
机译:车辆燃料电池中的氢气供应和循环对于它们的输出能力和寿命至关重要。在本文中,提出了一种基于车辆速度诱导的燃料电池电流扰动随机预测的氢调节提高了增强的多输入多输出(MIMO)模型预测控制(MPC)方案。首先为车速预测开发马尔可夫指数平滑定律。通过车辆动力学模型和规则的能量管理获得预测的燃料电池电力需求,以释放关于氢控制系统的扰动的预测堆栈电流。离散预测的电流序列具有随机特征,并键入MPC的预测模型,该预测模型越长,控制范围的长度。提出了两种案例研究以讨论在提升的增强MPC下讨论不同速度采样时间对氢调节结果的影响。增强的MPC具有比传统MPC更好的性能,并且在汽车驾驶循环期间,在案例2中,其控制RMSE可以在1和69.78%的情况下减少44.09%。循环(HIL)实验中的DSPACE MicroAutobox硬件,结果与模拟相匹配,验证了增强MPC方案的实时性能。

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