基于实时GRU非线性模型预测控制的氢-柴油双燃料发动机运行
Hydrogen-Diesel Dual-Fuel Engine Operation Using Real-Time GRU-Based Nonlinear Model Predictive Control
- RWTH Aachen University（亚琛工业大学）
- University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出基于GRU神经网络动力学的非线性模型预测控制，用于氢-柴油双燃料发动机瞬态控制，在99,800循环训练后，实验显示负载跟踪误差降低27.8%，PM减少61.1%，峰值氢能份额达77.8%，实现实时约束感知控制。
AI中文摘要：
氢-柴油双燃料（H2DF）燃烧可降低燃烧产生的CO2排放，但在高氢能量份额（HES）下表现出非线性的循环间动态特性。本研究评估了采用门控循环单元深度神经网络动力学模型的非线性模型预测控制（NMPC）用于瞬态H2DF控制。该模型在99,800个发动机循环上训练，可预测指示平均有效压力、氮氧化物（NOx）、颗粒物（PM）和最大压力升高率。单缸康明斯4.5升实验遵循一个未见过的4,900发动机循环轨迹。与生产用纯柴油控制相比，NMPC将负载跟踪平均绝对误差改善了27.8%，平均PM降低了61.1%，而在无废气再循环情况下，平均发动机出口NOx增加了105.1%。平均和峰值HES分别达到39.7%和55.1%；高氢设置实现了77.8%的峰值HES且无约束违规。学习动力学NMPC对反馈噪声和模型-工厂失配具有鲁棒性，并在低成本嵌入式硬件上每个发动机循环执行时间为3至7毫秒，从而实现了实用、实时、约束感知的瞬态H2DF控制，并大幅替代柴油。
英文摘要：
Hydrogen-diesel dual-fuel (H2DF) combustion reduces combustion-out CO2 emissions but exhibits nonlinear cycle-to-cycle dynamics at high hydrogen energy shares (HES). This work evaluates nonlinear model predictive control (NMPC) with a gated recurrent-unit deep neural network dynamics model for transient H2DF control. Trained on 99,800 engine cycles, the model predicts indicated mean effective pressure, nitrogen oxides (NOx), particulate matter (PM), and maximum pressure-rise rate. Single-cylinder Cummins 4.5 L experiments follow an unseen 4,900-engine-cycle trajectory. Compared with production diesel-only control, NMPC improves load-tracking mean absolute error by 27.8% and reduces mean PM by 61.1%, while mean engine-out NOx increases by 105.1% without exhaust-gas recirculation. Mean and peak HES reach 39.7% and 55.1%; a high-hydrogen setting achieves 77.8% peak HES without constraint violations. Robust to feedback noise and model-plant mismatch and executing in 3 to 7 ms per engine cycle on low-cost embedded hardware, learned-dynamics NMPC enables practical, real-time, constraint-aware transient H2DF control with substantial diesel substitution.
