ABSTRACT

Modern combustion engines require an efficient cycle-by-cycle fuel injection control scheme to optimise the single combustion events during transient operation. The online optimisation of the respective control inputs typically needs accurate while sufficiently simple models of the combustion quantities. Based on a recently presented cycle-by-cycle optimisation scheme with a hybrid model, this paper focuses on two aspects to enhance the accuracy as well as computational efficiency for an online computation. Firstly, the proper calibration of Gaussian processes nested in a combined physics-/data-based model structure is addressed. Respective test bench measurements and a tailored two-step training procedure are presented. Secondly, the computational efficiency of the online cycle-by-cycle optimisation is increased by mapping computationally intensive calculations into the data-based models through offline preprocessing. In addition, a data-driven approximation of the complete optimisation scheme is proposed to further minimise the computational demand. Simulation studies are used to evaluate the performance of these approaches.

摘要

现代内燃机需要有效的逐循环燃料喷射控制方案来优化瞬态运行期间的单个燃烧事件。各个控制输入的在线优化通常需要准确且足够简单的燃烧量模型。基于最近提出的具有混合模型的逐周期优化方案，本文重点关注两个方面来提高在线计算的准确性和计算效率。首先，解决了嵌套在基于物理/数据的组合模型结构中的高斯过程的正确校准问题。介绍了各自的测试台测量和量身定制的两步培训程序。第二，通过离线预处理将计算密集型计算映射到基于数据的模型中，提高了在线逐周期优化的计算效率。此外，提出了完整优化方案的数据驱动近似，以进一步最小化计算需求。模拟研究用于评估这些方法的性能。