Stabilization of the GCAI combustion process by in-cycle correlations

通过循环内相关性稳定 GCAI 燃烧过程

• 批准号: 317813673
• 负责人: Professor Dr.-Ing. Jakob Andert
• 金额: --
• 依托单位: Lehrstuhl für Thermodynamik mobiler Energiewandlungssysteme (TME)
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/317813673?language=en
• 关键词: Stabilization; GCAI; combustion; process; cycle

The need for mobility is constantly increasing. At the same time, the increasing release of anthropogenic CO2 from fossil sources and local air pollution are increasingly coming into the spotlight. Research on energy-efficient and low-emission propulsion systems can make an important contribution in this respect. Gasoline Controlled Auto Ignition (GCAI) is a promising approach to both increase efficiency and reduce pollutant emissions. However, the technical use of GCAI still faces unresolved challenges, such as the characteristic process characteristics with autoregressive cycle coupling and the dependence of combustion stability on the thermodynamic boundary conditions. Furthermore, the restriction of the engine operation map must be pointed out. In order to enable transient operation as well as to extend the possible operation range, the research is focused on concepts based on control engineering. Within this research unit, a novel optimization-based multi-scale control approach is followed to stabilize the GCAI combustion process by using in-cycle correlations. In the first project phase the potential of in-cycle control has already been successfully demonstrated.For model-based control, the prediction accuracy for stochastically occurring outlier cycles and the associated cycle coupling is of essential relevance. A main focus of the experimental work is the methodology for determining the autoregressive character of the combustion process by transient excitations. Due to the characterization as Markov process it is assumed that machine learning (e.g. reinforcement learning) has substantial advantages which cannot be achieved by conventional methods. Tailor-made algorithms for measuring the GCAI process will be developed to generate a broad database with transient data for system identification. A focus of the process modelling is on the emissions in order to be able to integrate them later into the cost function of nonlinear model-predictive control.The low-temperature kinetics is strongly dependent on the thermodynamic boundary conditions, which cannot be measured directly. Novel sensor concepts such as analysis of the ion current have the potential to determine the chemical/thermodynamic state in the cylinder more precisely and subsequently improve modelling and control. In cooperation with TP6 the ion current signal will be analyzed to use it as an additional input variable for the controller. Beyond the current state of research, information from the ion current sensor will be used to improve the models. Afterwards it will be investigated whether the ion current is suitable as an additional sensor quantity for integration into the controller. Finally, the control system will be validated in MiL and HiL operation.

对流动性的需求不断增加。与此同时，来自化石来源的人为二氧化碳排放量的增加和当地空气污染日益成为人们关注的焦点。对高能效和低排放推进系统的研究可在这方面作出重要贡献。汽油控制汽车点火是一种既能提高发动机效率又能减少污染物排放的有效方法。然而，GCAI的技术应用仍然面临着未解决的挑战，如自回归循环耦合的特征过程特性和燃烧稳定性对热力学边界条件的依赖。此外，必须指出发动机工作图的限制。为了使瞬态操作以及扩展可能的操作范围，研究的重点是基于控制工程的概念。在本研究单元中，一种新的基于优化的多尺度控制方法，其次是稳定的GCAI燃烧过程中使用的循环相关性。在项目的第一阶段，在循环控制的潜力已经被成功地证明。对于基于模型的控制，预测精度的随机发生的异常循环和相关的循环耦合是至关重要的。实验工作的一个主要重点是确定燃烧过程的自回归特性的瞬态激励的方法。由于马尔可夫过程的特征，假设机器学习（例如强化学习）具有传统方法无法实现的实质性优势。将开发用于测量GCAI过程的定制算法，以生成一个包含系统识别瞬态数据的广泛数据库。过程建模的重点是排放量，以便稍后能够将其整合到非线性模型预测控制的成本函数中。低温动力学强烈依赖于热力学边界条件，而热力学边界条件无法直接测量。新的传感器概念，如离子电流的分析，有可能更精确地确定在气缸中的化学/热力学状态，并随后改善建模和控制。与TP 6合作，将分析离子电流信号，以将其用作控制器的附加输入变量。除了目前的研究状态，来自离子电流传感器的信息将用于改进模型。然后，将研究离子电流是否适合作为集成到控制器中的附加传感器量。最后，控制系统将在MiL和HiL操作中进行验证。