The addition of new sensors and actuators to the engine, to reduce fuel consumption and emissions besides improving the engine operation, complicates the control commands stored in the engine control unit (ECU). Substitution of mechanical actuators with electronic ones increases the engine’s degrees of freedom and the number of control parameters, which results in the increased engine calibration time and cost. The aim of this paper is to take advantage of optimization techniques to achieve optimal values of control parameters in a fast and automated way. In this regard, it requires replacing the real engine with the virtual model and implementing the model-based calibration by coupling the virtual engine model with optimization algorithms. In this study, deep neural network (DNN) modeling and genetic algorithm (GA, NSGA-II) optimization are used for model-based calibration. The effect of all input control parameters, including ignition angle, continuously variable valve timing, etc., on all output control parameters including, brake-specific fuel consumption, emissions level, knock limit, combustion stability, etc., are investigated simultaneously by a valid global model, which is a remarkable achievement in the model-based calibration. Dynamic lag of some actuators delays the execution of control commands sent from ECU. To avoid abrupt variations in the actuators values, smoothness of the engine maps is considered in the calibration process. To reduce fuel consumption, decrease emission levels and attain smooth maps, the calibration of control parameters is performed by local-multi-objective optimization and global-single-objective optimization. Local-global model-based calibration presented in this study reduces 3.7% of the brake-specific fuel consumption and 7%–10% of emissions level at breakpoints of the engine map compared to manual calibration. In addition, the calibration time and costs while producing better engine performance can be reduced by automating the calibration process. Finally, calibrated maps are stored as a lookup table (LUT) in ECU. Generating an optimal lookup table involves the pre-calculation of several points that cover the calculation domain and allow the interpolation for other points. Selecting the optimal points for exact calculation is of great importance in the size and accuracy of LUT. In this study, an optimization tool is also presented to generate accurate and efficient LUT.

中文翻译：

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通过基于局部-全局模型的校准技术自动生成内燃机平滑图

在发动机中添加新的传感器和执行器，除了改善发动机运行之外，还可以减少油耗和排放，但同时也使存储在发动机控制单元 (ECU) 中的控制命令变得复杂。用电子执行器代替机械执行器增加了发动机的自由度和控制参数的数量，这导致发动机标定时间和成本增加。本文的目的是利用优化技术以快速、自动化的方式实现控制参数的最优值。对此，需要用虚拟模型替代真实发动机，通过将虚拟发动机模型与优化算法耦合来实现基于模型的标定。在本研究中，深度神经网络（DNN）建模和遗传算法（GA、NSGA-II）优化用于基于模型的校准。所有输入控制参数（包括点火角、连续可变气门正时等）对所有输出控制参数（包括制动特定燃油消耗、排放水平、爆震极限、燃烧稳定性等）的影响均通过有效的全局模型，这是基于模型的标定领域的一项了不起的成就。某些执行器的动态滞后会延迟 ECU 发送的控制命令的执行。为了避免执行器值突然变化，在校准过程中考虑了发动机图的平滑度。为了降低油耗、降低排放水平并获得平滑的地图，通过局部多目标优化和全局单目标优化来进行控制参数的标定。与手动校准相比，本研究中提出的基于局部-全局模型的校准可减少 3.7% 的制动特定燃油消耗和发动机图断点处的 7%–10% 的排放水平。此外，通过自动化校准过程，可以减少校准时间和成本，同时产生更好的发动机性能。最后，校准后的图作为查找表 (LUT) 存储在 ECU 中。生成最佳查找表涉及预先计算覆盖计算域的几个点并允许对其他点进行插值。选择最佳点进行精确计算对于LUT的大小和精度非常重要。在本研究中，还提出了一种优化工具来生成准确且高效的 LUT。