The process used by engine manufacturers for the development of a new engine includes the planning and conceptual design phases, followed by the detailed design phase, in which the design target specifications are met. In the conceptual design phase, a rough specification of the target engine is presented to facilitate a detailed design and the additional cost of modification is reduced exponentially. In the conceptual design phase, however, not only is there no real engine. but there are also no 1D and 3D models present, so it is impossible to test and simulate them. Therefore, at this stage, a model that can predict emission and performance only according to the specifications and operating conditions of the engine would be very useful. Previous studies developed an EGR prediction model that can be used in the 0-D NOx prediction using a deep learning method. In this study, a NOx prediction model with high accuracy using only the operating conditions as input variables, without ECU data, was developed using deep neural networks. The developed model has high accuracy with an R-square of 0.988. The feature of this model is that all the input parameters for the deep neural network come from the operating conditions of the engine. Therefore, this model can be used in the early stages of the development of new engines when testing and simulation cannot be performed because they do not exist. The designer can set the range of the operating conditions such that they do not exceed the NOx limits at the specific operating point (specific rpm and BMEP). This variable operating design methodology is expected to be useful in the development of new engines for automobile manufacturers with various engine data.

发动机制造商用于开发新发动机的过程包括计划和概念设计阶段，然后是详细设计阶段，在该阶段中，达到了设计目标规格。在概念设计阶段，提出了目标引擎的粗略说明，以促进详细设计，并且修改的额外成本呈指数减少。但是，在概念设计阶段，不仅没有真正的引擎。但是也没有1D和3D模型，因此无法测试和模拟它们。因此，在此阶段，仅根据发动机的规格和运行条件才能预测排放和性能的模型将非常有用。先前的研究开发了一种EGR预测模型，该模型可以使用深度学习方法在0-D NOx预测中使用。在这项研究中，使用深度神经网络开发了仅使用工况作为输入变量而没有ECU数据的高精度NOx预测模型。所开发的模型具有0.988的R平方，具有很高的精度。该模型的特征在于，深度神经网络的所有输入参数都来自发动机的运行条件。因此，该模型可以在新引擎开发的早期阶段使用，因为它们不存在，无法执行测试和仿真。设计者可以设置操作条件的范围，以使其在特定的工作点（特定的rpm和BMEP）下不超过NOx限值。