In this paper, optimization-oriented high fidelity indicated torque models which cover the whole operating regions under both steady-state and transient cycles for heavy-duty vehicles are developed. Two different experiments are performed and their data are merged to be utilized in the training of the models. In the first experiment, all combustion input channels are excited by quadratic chirp signals with different sweeps in their frequency profiles. Different from the first experiment, the engine speed is excited by ramp-hold signals in the second experiment. The estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer are utilized in the indicated torque calculations. In order to model the calculated indicated torque, a nonlinear finite impulse response (NFIR) model with a single layer sigmoid neural network has been designed. A sensitivity analysis is performed by generating several models with different number of input regressors and neurons. Experimental results show that the majority of the models in a selected wide range of the model parameters are validated with fit accuracies higher than 90 % and 85 % on the World Harmonized Stationary Cycle (WHSC) and the World Harmonic Transient Cycle (WHTC), respectively.

中文翻译：

---

面向优化的高保真NFIR模型，用于估计柴油发动机的指示扭矩。

在本文中，开发了面向优化的高保真指示扭矩模型，该模型涵盖了重型车辆在稳态和瞬态循环下的整个工作区域。进行了两个不同的实验，并将它们的数据合并以用于模型的训练。在第一个实验中，所有燃烧输入通道均由二次线性调频信号激励，其频率曲线具有不同的扫描范围。与第一个实验不同，在第二个实验中，发动机转速是由斜坡保持信号激发的。在指示的扭矩计算中，除了从发动机测功机测得的扭矩外，还估算了摩擦，泵送和惯性扭矩。为了对计算出的指示扭矩建模，设计了具有单层S形神经网络的非线性有限脉冲响应（NFIR）模型。通过生成具有不同数量的输入回归变量和神经元的几个模型来执行敏感性分析。实验结果表明，在选定的广泛模型参数中，大多数模型在世界协调平稳周期（WHSC）和世界协调瞬态周期（WHTC）上的拟合精度分别高于90％和85％ 。