The engine development process faces big challenges from new strict emission regulations in addition to the need for fuel efficiency improvements. The Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments decreases the required time during engine development, calibration, verification, and validation of the product. An accurate and easy to build dyno-engine model with real-time operational ability is required for this purpose. Artificial Neural Networks (ANN) have shown ability to model dynamic and complex systems like internal combustion engines. In this paper, the Group Method of Data Handling (GMDH) algorithm was utilized to build an ANN model of a heavy-duty diesel engine. One objective is to reduce the amount of manual labor on the results during the ANN model development process. The GMDH algorithm is a self-organizing process that will find the system laws and optimize the model structure automatically in one iteration. The GMDH model results were compared with a model developed by Levenberg-Marquardt Backpropagation (LM-BP) algorithm. The ANN models used actuator signals from an Engine Management System (EMS) to simulate the engine operation parameters. As revealed by the simulation results, the ANN models successfully predicted engine torque, fuel flow, and NOx concentration. The GMDH model as a self-organized model reduced lead time, had slightly higher transient cycle accuracy, had fewer inconsistent predictions, and demonstrated better extrapolation capability. The prediction accuracy for transient operation was improved by shifting the predicted value by calculating time delay and a decrease of 76.66% for fuel flow and 66.51% for NOX concentration in model accuracy were achieved. The GMDH dyno-engine model can be effectively applied as a virtual test cell instrument for testing, calibration, and optimization purposes.

中文翻译：

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基于GMDH型神经网络的重型柴油机虚拟测试单元设计

除了需要提高燃油效率外，发动机开发过程还面临来自新的严格排放法规的巨大挑战。软件在环 (SiL) 和硬件在环 (HiL) 环境减少了发动机开发、校准、验证和产品验证所需的时间。为此，需要一个准确且易于构建的具有实时操作能力的动力引擎模型。人工神经网络 (ANN) 已显示出对内燃机等动态和复杂系统进行建模的能力。在本文中，数据处理的群方法（GMDH）算法被用来建立一个重型柴油机的人工神经网络模型。一个目标是在 ANN 模型开发过程中减少对结果的人工劳动量。GMDH 算法是一个自组织过程，它会在一次迭代中自动找到系统规律并优化模型结构。将 GMDH 模型结果与由 Levenberg-Marquardt 反向传播 (LM-BP) 算法开发的模型进行比较。ANN 模型使用来自发动机管理系统 (EMS) 的执行器信号来模拟发动机运行参数。正如模拟结果所揭示的那样，ANN 模型成功地预测了发动机扭矩、燃料流量和 NOx 浓度。GMDH 模型作为自组织模型缩短了前置时间，瞬态周期精度略高，不一致的预测较少，并表现出更好的外推能力。通过计算时间延迟和减少 76 来移动预测值，提高了瞬态运行的预测精度。模型精度达到了 66% 的燃料流量和 66.51% 的 NOX 浓度。GMDH dyno-engine 模型可以有效地用作用于测试、校准和优化目的的虚拟测试单元仪器。